Generic robot architecture

ABSTRACT

The present invention provides methods, computer readable media, and apparatuses for a generic robot architecture providing a framework that is easily portable to a variety of robot platforms and is configured to provide hardware abstractions, abstractions for generic robot attributes, environment abstractions, and robot behaviors. The generic robot architecture includes a hardware abstraction level and a robot abstraction level. The hardware abstraction level is configured for developing hardware abstractions that define, monitor, and control hardware modules available on a robot platform. The robot abstraction level is configured for defining robot attributes and provides a software framework for building robot behaviors from the robot attributes. Each of the robot attributes include hardware information from at least one hardware abstractions. In addition, each robot attribute is configured to substantially isolate the robot behaviors from the hardware abstractions.

CONTRACTUAL ORIGIN OF THE INVENTION

The United States Government has rights in the following inventionpursuant to Contract No. DE-AC07-05-ID14517 between the U.S. Departmentof Energy and Battelle Energy Alliance, LLC.

BACKGROUND OF THE INVENTION

Field of the Invention: The present invention relates generally torobotics and, more specifically, to software architectures for realizingan intelligence kernel for robots.

State of the Art: Historically, robot behaviors have been created forspecific tasks and applications. These behaviors have generally beenreinvented time and again for different robots and differentapplications. There has been no sustained attempt to provide a kernel ofbasic robot competence and decision making that can be used to bootstrapdevelopment across many different applications.

Some architectures have been proposed that provide a generic applicationprogramming interface (API) for querying various sensors and commandingvarious actuators; however, many of these architectures have beenlimited to raw inputs and outputs rather than provide the intelligenceand behavior to a robot. As a result, the behavior functionality createdfor one robot may not be easily ported to new robots. Otherarchitectures have been proposed to allow limited behaviors to portacross different robot platforms, but these have generally been limitedto specific low-level control systems.

The problem with robots today is that they are not very bright. Currentrobot “intelligence” is really just a grab-bag of programmed behaviorsto keep mobile robots from doing stupid things, like getting stuck incorners or running into obstacles. The promise of wireless robots isthat they can be sent into remote situations that are too difficult ordangerous for humans. The reality is that today's robots generally lackthe ability to make any decisions on their own and rely on continuousguidance by human operators watching live video from on-board cameras.

Most commercial robots operate on a master/slave principle. A humanoperator completely controls the movement of the robot from a remotelocation using robot-based sensors such as video and Global PositioningSystem (GPS). This setup often requires more than one operator per robotto navigate around obstacles and achieve a goal. As a result veryskilled operators may be necessary to reliably direct the robot.Furthermore, the intense concentration needed for controlling the robotcan detract from achieving mission goals.

Although it has been recognized that there is a need for adjustableautonomy, robot architectures currently do not exist that provide afoundation of autonomy levels upon which to build intelligent roboticcapabilities. Furthermore, robot architectures do not currently existsthat provides a foundation of generic robot attributes for porting to avariety of robot platforms.

Therefore, there is a need for a generic robot architecture thatprovides a framework that is easily portable to a variety of robotplatforms and is configured to not only provide hardware abstractionsbut also provide abstractions for generic robot attributes and robotbehaviors.

In addition, there is a need for a robot intelligence kernel thatprovides a framework of dynamic autonomy that is easily portable to avariety of robot platforms and is configured to control a robot at avariety of interaction levels and across a diverse range of robotbehaviors.

BRIEF SUMMARY OF THE INVENTION

The present invention provides methods, computer readable media, andapparatuses for a generic robot architecture that provides a frameworkthat is easily portable to a variety of robot platforms and isconfigured to not only provide hardware abstractions but also provideabstractions for generic robot attributes, environment abstractions, androbot behaviors.

An embodiment of the present invention comprises a method for providinga generic robot architecture for robot control software. The methodincludes providing a hardware abstraction level and providing a robotabstraction level. The hardware abstraction level is configured fordeveloping a plurality of hardware abstractions that define, monitor,and control a plurality of hardware modules available on a robotplatform. The robot abstraction level is configured for defining aplurality of robot attributes and provides a software framework forbuilding robot behaviors from the plurality of robot attributes. Each ofthe robot attributes include hardware information from at least one ofthe plurality of hardware abstractions. In addition, each robotattribute is configured to substantially isolate the robot behaviorsfrom the plurality of hardware abstractions.

Another embodiment of the present invention comprises a computerreadable medium having computer executable instructions thereon, whichwhen executed on a processor provide a generic robot architecture. Thegeneric robot architecture includes a hardware abstraction level and arobot abstraction level. The hardware abstraction level is configuredfor developing a plurality of hardware abstractions that define,monitor, and control a plurality of hardware modules available on arobot platform. The robot abstraction level is configured for defining aplurality of robot attributes and provides a software framework forbuilding robot behaviors from the plurality of robot attributes. Each ofthe robot attributes include hardware information from at least one ofthe plurality of hardware abstractions. In addition, each robotattribute is configured to substantially isolate the robot behaviorsfrom the plurality of hardware abstractions.

Another embodiment of the present invention comprises a robot platformincluding at least one perceptor configured for perceiving environmentalvariables of interest, at least one locomotor configured for providingmobility to the robot platform, and a system controller configured forexecuting a generic robot architecture. The generic robot architectureincludes a hardware abstraction level and a robot abstraction level. Thehardware abstraction level is configured for developing a plurality ofhardware abstractions that define, monitor, and control a plurality ofhardware modules available on the robot platform. The robot abstractionlevel is configured for defining a plurality of robot attributes andprovides a software framework for building robot behaviors from theplurality of robot attributes. Each of the robot attributes includehardware information from at least one of the plurality of hardwareabstractions. In addition, each robot attribute is configured tosubstantially isolate the robot behaviors from the plurality of hardwareabstractions

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which illustrate what is currently considered to be thebest mode for carrying out the invention:

FIG. 1 illustrates a representative robot platform embodiment of thepresent invention;

FIG. 2 illustrates a representative robot control environment includinga plurality of robot platforms and a robot controller;

FIG. 3 is a software architecture diagram illustrating significantcomponents of embodiments of the present invention;

FIG. 4 illustrates representative hardware abstractions of hardwaremodules that may be available on robot platforms;

FIG. 5 illustrates a robot abstraction level including robot attributesthat may be available on robot platforms;

FIG. 6 illustrates a representative embodiment of how a rangeabstraction may be organized;

FIG. 7 illustrates an occupancy grid map that may be developed byembodiments of the present invention;

FIG. 8 illustrates representative robot behavioral components that maybe available on robot platforms;

FIG. 9 illustrates representative cognitive conduct components that maybe available on robot platforms;

FIG. 10A illustrates how tasks may be allocated between an operator anda robot according to embodiments of the present invention;

FIG. 10B illustrates various cognitive conduct, robot behaviors, robotattributes, and hardware abstractions that may be available at differentlevels of robot autonomy;

FIG. 11 illustrates a portion of representative processing the may occurin developing robot attributes and communicating those attributes;

FIG. 12 illustrates a representative example of communication pathsbetween various hardware abstraction, robot abstraction, and environmentabstractions;

FIG. 13 illustrates a representative example of communication pathsbetween robot abstractions, environment abstractions, robot behaviors,and robot conduct;

FIG. 14 is a software flow diagram illustrating components of analgorithm for performing a guarded motion behavior;

FIG. 15 is a software flow diagram illustrating components of analgorithm for performing translational portions of an obstacle avoidancebehavior;

FIG. 16 is a software flow diagram illustrating components of analgorithm for performing rotational portions of the obstacle avoidancebehavior;

FIG. 17 is a software flow diagram illustrating components of analgorithm for performing a get unstuck behavior;

FIG. 18 is a software flow diagram illustrating components of analgorithm for performing a real-time occupancy change analysis behavior;

FIG. 19 is a block diagram of a robot system for implementing a virtualtrack for a robot, in accordance with an embodiment of the presentinvention;

FIG. 20 illustrates a user interface for designating a desired pathrepresentative of a virtual track for a robot, in accordance of with anembodiment of the present invention;

FIG. 21 is a process diagram for configuring the desired path into awaypoint file for execution by a robot, in accordance with an embodimentof the present invention;

FIG. 22 illustrates a user interface for further processing the desiredpath into a program for execution by a robot, in accordance with anembodiment of the present invention;

FIG. 23 is a diagram illustrating transformation from a drawing file toa program or waypoint file, in accordance with an embodiment of thepresent invention;

FIG. 24 is a process diagram of a control process of a robot, inaccordance with an embodiment of the present invention;

FIG. 25 is a flowchart of a method for implementing a virtual track fora robot, in accordance with an embodiment of the present invention;

FIG. 26 is a software flow diagram illustrating components of analgorithm for handling a waypoint follow behavior:

FIG. 27 is a software flow diagram illustrating components of analgorithm for performing translational portions of the waypoint followbehavior;

FIG. 28 is a software flow diagram illustrating components of analgorithm for performing rotational portions of the waypoint followbehavior;

FIG. 29 is a software flow diagram illustrating components of analgorithm for performing a follow conduct;

FIGS. 30A and 30B are a software flow diagram illustrating components ofan algorithm for performing a countermine conduct;

FIG. 31 is a block diagram of a robot system, in accordance with anembodiment of the present invention;

FIG. 32 illustrates a multi-robot user interface for operatorinteraction, in accordance with an embodiment of the present invention;

FIG. 33 illustrates a video window of the multi-robot user interface, inaccordance with an embodiment of the present invention;

FIG. 34 illustrates a sensor status window of the multi-robot userinterface, in accordance with an embodiment of the present invention;

FIG. 35 illustrates an autonomy control window of the multi-robot userinterface, in accordance with an embodiment of the present invention;

FIG. 36 illustrates a robot window of the multi-robot user interface, inaccordance with an embodiment of the present invention;

FIG. 37 illustrates an emerging map window of the multi-robot userinterface, in accordance with an embodiment of the present invention;

FIG. 38 illustrates a dashboard window of the multi-robot userinterface, in accordance with an embodiment of the present invention;and

FIG. 39 illustrates control processes within the robots and userinterface system, in accordance with an embodiment of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides methods and apparatuses for a robotintelligence kernel that provides a framework of dynamic autonomy thatis easily portable to a variety of robot platforms and is configured tocontrol a robot at a variety of interaction levels and across a diverserange of robot behaviors.

In the following description, circuits and functions may be shown inblock diagram form in order not to obscure the present invention inunnecessary detail. Conversely, specific circuit implementations shownand described are exemplary only and should not be construed as the onlyway to implement the present invention unless specified otherwiseherein. Additionally, block definitions and partitioning of logicbetween various blocks is exemplary of a specific implementation. Itwill be readily apparent to one of ordinary skill in the art that thepresent invention may be practiced by numerous other partitioningsolutions. For the most part, details concerning timing considerationsand the like have been omitted where such details are not necessary toobtain a complete understanding of the present invention and are withinthe abilities of persons of ordinary skill in the relevant art.

In this description, some drawings may illustrate signals as a singlesignal for clarity of presentation and description. It will beunderstood by a person of ordinary skill in the art that the signal mayrepresent a bus of signals, wherein the bus may have a variety of bitwidths and the present invention may be implemented on any number ofdata signals including a single data signal.

Furthermore, in this description of the invention, reference is made tothe accompanying drawings which form a part hereof, and in which isshown, by way of illustration, specific embodiments in which theinvention may be practiced. The embodiments are intended to describeaspects of the invention in sufficient detail to enable those skilled inthe art to practice the invention. Other embodiments may be utilized andchanges may be made without departing from the scope of the presentinvention. The following detailed description is not to be taken in alimiting sense, and the scope of the present invention is defined onlyby the appended claims.

Headings are included herein to aid in locating certain sections ofdetailed description. These headings should not be considered to limitthe scope of the concepts described under any specific heading.Furthermore, concepts described in any specific heading are generallyapplicable in other sections throughout the entire specification.

1. Hardware Environment

FIG. 1 illustrates a representative robot platform 100 (may also bereferred to herein as a robot system) including the present invention. Arobot platform 100 may include a system controller 110 including asystem bus 150 for operable coupling to one or more communicationdevices 155 operably coupled to one or more communication channels 160,one or more perceptors 165, one or more manipulators 170, and one ormore locomotors 175.

The system controller 110 may include a processor 120 operably coupledto other system devices by internal busses (122, 124). By way ofexample, and not limitation, the processor 120 may be coupled to amemory 125 through a memory bus 122. The system controller 110 may alsoinclude an internal bus 124 for coupling the processor 120 to variousother devices, such as storage devices 130, local input devices 135,local output devices 140, and local displays 145.

Local output devices 140 may be devices such as speakers, status lights,and the like. Local input devices 135 may be devices such as keyboards,mice, joysticks, switches, and the like.

Local displays 145 may be as simple as light-emitting diodes indicatingstatus of functions of interest on the robot platform 100, or may be ascomplex as a high resolution display terminal.

The communication channels 160 may be adaptable to both wired andwireless communication, as well as supporting various communicationprotocols. By way of example, and not limitation, the communicationchannels may be configured as a serial or parallel communicationchannel, such as, for example, USB, IEEE-1394, 802.11a/b/g, cellulartelephone, and other wired and wireless communication protocols.

The perceptors 165 may include inertial sensors, thermal sensors,tactile sensors, compasses, range sensors, sonar, Global PositioningSystem (GPS), Ground Penetrating Radar (GPR), lasers for objectdetection and range sensing, imaging devices, and the like. Furthermore,those of ordinary skill in the art will understand that many of thesesensors may include a generator and a sensor to combine sensor inputsinto meaningful, actionable perceptions. For example, sonar perceptorsand GPR may generate sound waves or sub-sonic waves and sense reflectedwaves. Similarly, perceptors including lasers may include sensorsconfigured for detecting reflected waves from the lasers for determininginterruptions or phase shifts in the laser beam.

Imaging devices may be any suitable device for capturing images, suchas, for example, an infrared imager, a video camera, a still camera, adigital camera, a Complementary Metal Oxide Semiconductor (CMOS) imagingdevice, a charge coupled device (CCD) imager, and the like. In addition,the imaging device may include optical devices for modifying the imageto be captured, such as, for example, lenses, collimators, filters, andmirrors. For adjusting the direction at which the imaging device isoriented, a robot platform 100 may also include pan and tilt mechanismscoupled to the imaging device. Furthermore, a robot platform 100 mayinclude a single imaging device or multiple imaging devices.

The manipulators 170 may include vacuum devices, magnetic pickupdevices, arm manipulators, scoops, grippers, camera pan and tiltmanipulators, and the like.

The locomotors 175 may include one or more wheels, tracks, legs,rollers, propellers, and the like. For providing the locomotive powerand steering capabilities, the locomotors 175 may be driven by motors,actuators, levers, relays and the like. Furthermore, perceptors 165 maybe configured in conjunction with the locomotors 175, such as, forexample, odometers and pedometers.

FIG. 2 illustrates a representative robot control environment includinga plurality of robot platforms (100A, 100B, and 100C) and a robotcontroller 180. The robot controller 180 may be a remote computerexecuting a software interface from which an operator may control one ormore robot platforms (100A, 100B, and 100C) individually or incooperation. The robot controller 180 may communicate with the robotplatforms (100A, 100B, and 100C), and the robot platforms (100A, 100B,and 100C) may communicate with each other, across the communicationchannels 160. While FIG. 2 illustrates one robot controller 180 andthree robot platforms (100A, 100B, and 100C) those of ordinary skill inthe art will recognize that a robot control environment may include oneor more robot platforms 100 and one or more robot controllers 180. Inaddition, the robot controller 180 may be a version of a robot platform100.

Software processes illustrated herein are intended to illustraterepresentative processes that may be performed by the robot platform 100or robot controller 180. Unless specified otherwise, the order in whichthe processes are described is not intended to be construed as alimitation. Furthermore, the processes may be implemented in anysuitable hardware, software, firmware, or combinations thereof. By wayof example, software processes may be stored on the storage device 130,transferred to the memory 125 for execution, and executed by theprocessor 120.

When executed as firmware or software, the instructions for performingthe processes may be stored on a computer readable medium (i.e., storagedevice 130). A computer readable medium includes, but is not limited to,magnetic and optical storage devices such as disk drives, magnetic tape,CDs (compact disks), DVDs (digital versatile discs or digital videodiscs), and semiconductor devices such as RAM, DRAM, ROM, EPROM, andFlash memory.

2. Generic Robot Abstraction Architecture

Conventionally, robot architectures have been defined for individualrobots and generally must be rewritten or modified to work withdifferent sensor suites and robot platforms. This means that adaptingthe behavior functionality created for one robot platform to a differentrobot platform is problematic. Furthermore, even architectures thatpropose a hardware abstraction layer to create a framework for acceptingvarious hardware components still may not create a robot abstractionlayer wherein the abstractions presented for high level behavioralprogramming are in terms of actionable components or generic robotattributes rather than the hardware present on the robot.

A notable aspect of the present invention is that it collates the sensordata issued from hardware or other robotic architectures into actionableinformation in the form of generic precepts. Embodiments of the presentinvention may include a generic robot architecture (GRA), whichcomprises an extensible, low-level framework, which can be appliedacross a variety of different robot hardware platforms, perceptorsuites, and low-level proprietary control application programminginterfaces (APIs). By way of example, some of these APIs may beMobility, Aria, Aware, Player, etc.).

FIG. 3 is a software architecture diagram illustrating significantcomponents of the GRA as a multi-level abstraction. Within the GRA,various levels of abstraction are available for use in developing robotbehavior. The object oriented structure of the GRA may be thought of asa including two basic levels. As is conventional in object orientedclass structures, each subsequent level inherits all of thefunctionality of the higher levels.

At the lower level, the GRA includes a hardware abstraction level, whichprovides for portable, object oriented access to low level hardwareperception and control modules that may be present on a robot. Thehardware abstraction level is reserved for hardware specific classes andincludes, for example, implementations for the actual robot geometry andsensor placement on each robot type.

Above the hardware abstraction level, the GRA includes a robotabstraction level, which provides atomic elements (i.e., buildingblocks) of generic robot attributes and develops a membrane between thelow level hardware abstractions and controls. This membrane is based ongeneric robot attributes, or actionable components, which include robotfunctions, robot perceptions, and robot status. Each generic robotattribute may utilize a variety of hardware abstractions, and possiblyother robot attributes, to accomplish its individual function.

The robot abstraction level may include implementations that are genericto given proprietary low level APIs. Examples of functions in this classlevel include the interface calls for a variety of atomic level robotbehaviors such as, for example, controlling motion and reading sonardata.

The GRA enables substantially seamless porting of behavioralintelligence to new hardware platforms and control APIs by defininggeneric robot attributes and actionable components to provide themembrane and translation between behavioral intelligence and thehardware. Once a definition for a robot in terms of platform geometries,sensors, and API calls has been specified, behavior and intelligence maybe ported in a substantially seamless manner for future development. Inaddition, the object oriented structure enables straightforwardextension of the architecture for defining new robot platforms as wellas defining low-level abstractions for new perceptors, motivators,communications channels, and manipulators.

The GRA includes an interpreter such that existing and new robotbehaviors port in a manner that is transparent to both the operator andthe behavior developer. This interpreter may be used to translatecommands and queries back and forth between the operator and robot witha common interface, which can then be used to create perceptualabstractions and behaviors. When the “common language” supported by theGRA is used by robot developers, it enables developed behaviors andfunctionality to be interchangeable across multiple robots. In additionto creating a framework for developing new robot capabilities, the GRAinterpreter may be used to translate existing robot capabilities intothe common language so that the behavior can then be used on otherrobots. The GRA is portable across a variety of platforms andproprietary low level APIs. This is done by creating a standard methodfor commanding and querying robot functionality that exists on top ofany particular robot manufacturers control API. Moreover, unlike systemswhere behavior stems from sensor data the GRA facilitates a consistentor predictable behavior output regardless of robot size or type bycategorizing robot and sensor data into perceptual abstractions fromwhich behaviors can be built.

The Generic Robot Architecture also includes a scripting structure fororchestrating the launch of the different servers and executables thatmay be used for running the GRA on a particular robot platform. Notethat since these servers and executables (e.g., laser server, cameraserver, and base platform application) will differ from robot to robot,the scripting structure includes the ability to easily specify andcoordinate the launch of the files that may be needed for specificapplications. In addition, the scripting structure enables automaticlaunching of the system at boot time so that the robot is able toexhibit functionality without any operator involvement (i.e., no needfor a remote shell login).

The Generic Robot Architecture may access configuration files createdfor each defined robot type. For example, the configuration files mayspecify what sensors, actuators, and API are being used on a particularrobot. Use of the scripting structure together with the configurationenables easy reconfiguration of the behaviors and functionality of therobot without having to modify source code (i.e., for example, recompilethe C/C++ code).

The GRA keeps track of which capabilities are available (e.g., sensors.actuators, mapping systems, communications) on the specific embodimentand uses virtual and stub functions within the class hierarchy to ensurethat commands and queries pertaining to capabilities that an individualrobot does not have do not cause data access errors. For example, in acase where a specific capability, such as a manipulator, does not exist,the GRA returns special values indicating to the high-level behavioralcontrol code that the command cannot be completed or that the capabilitydoes not exist. This makes it much easier to port seamlessly betweendifferent robot types by allowing the behavior code to adaptautomatically to different robot configurations.

The above discussion of GRA capabilities has focused on therobot-oriented aspects of the GRA. However, the robot-oriented classstructure is only one of many class structures included in the GRA. Forexample, the GRA also includes multi-tiered class structures forcommunication, range-sensing, cameras, and mapping. Each one of theseclass structures is set up to provide a level of functional modularityand allow different sensors and algorithms to be used interchangeably.By way of example, and not limitation, without changing the behavioralcode built on the GRA at the robot behavior level, it may be possible toswap various mapping and localization systems or cameras and yet achievethe same functionality simply by including the proper class modules atthe hardware abstraction level and possibly at the robot abstractionlevel. Additional capabilities and features of each of the levels of theGRA are discussed below.

2.1. Hardware Abstraction Level

FIG. 4 illustrates the hardware abstraction level 210, which includesrepresentative hardware abstractions of hardware modules that may beavailable on a robot platform. These hardware abstractions create anobject oriented interface between the software and hardware that ismodular, reconfigurable, and portable across robot platforms. As aresult, a software component can create a substantially generic hook toa wide variety of hardware that may perform a similar function. It willbe readily apparent to those of ordinary skill in the art that themodules shown in FIG. 4 are a representative, rather than comprehensiveexample of hardware abstractions. Some of these hardware abstractionsinclude; action abstractions 212(also referred to as manipulationabstractions) for defining and controlling manipulation type devices onthe robot, communication abstractions 214 for defining and controllingcommunication media and protocols, control abstractions 216 (alsoreferred to as locomotion abstractions) for defining and controllingmotion associated with various types of locomotion hardware, andperception abstractions 218 for defining and controlling a variety ofhardware modules configured for perception of the robot's surroundingsand pose (i.e., position and orientation).

2.1.1. Manipulation Abstractions

Action device abstractions 212 may include, for example, vacuum devices,magnetic pickup devices, arm manipulators, scoops, grippers, camera panand tilt manipulators, and the like.

2.1.2. Communications Abstractions

The Communication abstractions present substantially commoncommunications interfaces to a variety of communication protocols andphysical interfaces. The communication channels 160 may be adaptable toboth wired and wireless communication, as well as supporting variouscommunication protocols. By way of example, and not limitation, thecommunication abstractions may be configured to support serial andparallel communication channels, such as, for example, USB, IEEE-1394,802.11a/b/g, cellular telephone, and other wired and wirelesscommunication protocols.

2.1.3. Locomotion Abstractions

Locmotion abstractions 216 may be based on robot motion, not necessarilyon specific hardware components. For example, and not limitation, motioncontrol abstractions may include drive, steering, power, speed, force,odometery, and the like. Thus, the motion abstractions can be tailoredto individual third party drive controls at the hardware abstractionlevel and effectively abstracted away from other architecturalcomponents. In this manner, support for motion control of a new robotplatform may comprise simply supplying the APIs which control the actualmotors, actuators and the like, into the locomotion abstractionframework.

2.1.4. Perception Abstractions

The perception abstractions 218 may include abstractions for a varietyof perceptive hardware useful for robots, such as, for example, inertialmeasurements, imaging devices, sonar measurements, camera pan/tiltabstractions, GPS and iGPS abstractions, thermal sensors, infraredsensors, tactile sensors, laser control and perception abstractions,GPR, compass measurements, EMI measurements, and range abstractions.

2.2. Robot Abstraction Level

While the hardware abstraction level 210 focuses on a software model fora wide variety of hardware that may be useful on robots, the robotabstraction level 230 (as illustrated in FIGS. 3 and 5) focuses ongeneric robot attributes. The generic robot attributes enable buildingblocks for defining robot behaviors at the robot behavior level andprovide a membrane for separating the definition of robot behaviors fromthe low level hardware abstractions. Thus, the each robot attributes mayutilize one or more hardware abstractions to define its attribute. Theserobot attributes may be thought of as actionable abstractions. In otherwords, a given actionable abstraction may fuse multiple hardwareabstractions that provide similar information into a data set for aspecific robot attribute. For example, and not limitation, the genericrobot attribute of “range” may fuse range data from hardwareabstractions of an IR sensor and a laser sensor to present a singlecoherent structure for the range attribute. In this way, the GRApresents robot attributes as building blocks of interest for creatingrobot behaviors such that, the robot behavior can use the attribute todevelop a resulting behavior (e.g., stop, slow down, turn right, turnleft, etc).

Furthermore, a robot attribute may combine information from dissimilarhardware abstractions. By way of example, and not limitation, theposition attributes may fuse information from a wide array of hardwareabstractions, such as: perception modules like video, compass, GPS,laser, and sonar; along with control modules like drive, speed, andodometery. Similarly, a motion attribute may include information fromposition, inertial, range, and obstruction abstractions.

This abstraction of robot attributes frees the developer from dealingwith individual hardware elements. In addition, each robot attribute canadapt to the amount, and type of information it incorporates into theabstraction based on what hardware abstractions may be available on therobot platform.

The robot attributes, as illustrated in FIG. 5, are defined at arelatively low level of atomic elements that include attributes ofinterest for a robot's perception, status, and control. Some of theserobot attributes include; robot health 232, robot position 234, robotmotion 236, robot bounding shape 238, environmental occupancy grid 240,and range 242. It will be readily apparent to those of ordinary skill inthe art that the modules shown in FIG. 5 are a representative, ratherthan comprehensive, example of robot attributes. Note that the termrobot attributes is used somewhat loosely, given that robot attributesmay include physical attributes such as health 232 and bounding shape238 as well as how the robot perceives its environment, such as theenvironmental occupancy grid 240 and range attributes 242.

2.2.1. Robot Health

The robot health abstractions 232 may include, for example, generalobject models for determining the status and presence of various sensorsand hardware modules, determining the status and presence of variouscommunication modules, determining the status of on board computercomponents.

2.2.2. Robot Bounding Shape

The robot bounding shape 238 abstractions may include, for example,definitions of the physical size and boundaries of the robot anddefinitions of various thresholds for movement that define a safety zoneor event horizon, as is explained more fully below.

2.2.3. Robot Motion

The robot motion abstractions 236 may include abstractions for definingrobot motion and orientation attributes such as, for example, obstructedmotion, velocity, linear and angular accelerations, forces, and bumpinto obstacle, and orientation attributes such as roll, yaw and pitch.

2.2.4. Range

The range abstractions 242 may include, for example, determination ofrange to obstacles from lasers, sonar, infra-red, and fused combinationsthereof.

In more detail, FIG. 6 illustrates a representative embodiment of how arange abstraction may be organized. A variety of coordinate systems maybe in use by the robot and an operator. By way of example, a localcoordinate system may be defined by an operator relative to a space ofinterest (e.g., a building) or a world coordinate system defined bysensors such as a GPS unit, an iGPS unit, a compass, an altimeter, andthe like. A robot coordinate system may be defined in Cartesiancoordinates relative to the robots orientation such that, for example,the X axis is to the right, the Y axis is straight ahead, and the Z-axisis up. Another robot coordinate system may be cylindrical coordinateswith a range, angle, and height relative to the robot's currentorientation.

The range measurements for the representative embodiment illustrated inFIG. 6 are organized in a cylindrical coordinate system relative to therobot. The angles may be partitioned into regions covering the front,left, right and back of the robot and given names such as, for example,those used in FIG. 6.

Thus, regions in front may be defined and named as:

-   -   Right_In_Front (310 and 310′), representing an angle between        −15° and 15°;    -   Front 312, representing an angle between −45° and 45°; and    -   Min_Font_Dist 314, representing an angle between −90° and 90°.

Similarly, regions to the left side may be defined as:

-   -   Left_Side 320, representing an angle between 100° and 80°;

1Left_Front 322, representing an angle between 60° and 30°;

-   -   Front_Left_Side 324, representing an angle between 70° and 50°;        and    -   L_Front 326, representing an angle between 45 and 1°.

For the right side, regions may be defined as:

-   -   Right_Side 330, representing an angle between −100° and −80°;    -   Right_Front 332, representing an angle between −60° and −30°;    -   Front_Right_Side 334, representing an angle between −70° and        −50°; and    -   R_Front 336, representing an angle between −45 and 0°.

While not shown, those of ordinary skill in the art will recognize thatwith the exception of the Left_Side 320 and Right_Side 330 regions,embodiments may include regions in the back, which are a mirror image ofthose in the front wherein the “Front” portion of the name is replacedwith “Rear.”

Furthermore, the range attributes define a range to the closest objectwithin that range. However, the abstraction of regions relative to therobot, as used in the range abstraction may, also be useful for manyother robot attributes and robot behaviors that may require directionalreadings, such as, for example, defining robot position, robot motion,camera positioning, an occupancy grid map, and the like.

In practice, the range attributes may be combined to define a morespecific direction. For example, directly forward motion may be definedas a geometrically adjusted combination of Right_In_Front 310, L_Front326, R_Front 336, Front_Left_Side 324, and Front_right_side 334.

2.2.5. Robot Position and Environmental Occupancy Grid Maps

Returning to FIG. 5, the robot abstractions may include positionattributes 234. Mobile robots may operate effectively only if they, ortheir operators, know where they are. Conventional robots may rely onreal-time video and global positioning systems (GPS) as well as existingmaps and floor plans to determine their location. However, GPS may notbe reliable indoors and video images may be obscured by smoke or dust,or break up because of poor communications. Maps and floor plans may notbe current and often are not readily available, particularly in thechaotic aftermath of natural, accidental or terrorist events.Consequently, real-world conditions on the ground often makeconventional robots that rely on a priori maps ineffective.

Accurate positioning knowledge enables the creation of high-resolutionmaps and accurate path following, which may be needed for high-leveldeliberative behavior such as systematically searching or patrolling anarea.

Embodiments of the present invention may utilize various mapping orlocalization techniques including positioning systems such as indoorGPS, outdoor GPS, differential GPS, theodolite systems, wheel-encoderinformation, and the like. To make robots more autonomous, embodimentsof the present invention may fuse the mapping and localizationinformation to build 3D maps on-the-fly that let robots understand theircurrent position and an estimate of their surroundings. Using existinginformation, map details may be enhanced as the robot moves through theenvironment. Ultimately, a complete map containing rooms, hallways,doorways, obstacles and targets may be available for use by the robotand its human operator. These maps also may be shared with other robotsor human first responders.

With the on-board mapping and positioning algorithm that accepts inputfrom a variety of range sensors, the robot may make substantiallyseamless transitions between indoor and outdoor operations withoutregard for GPS and video drop-outs that occur during these transitions.Furthermore, embodiments of the present invention provide enhanced faulttolerance because they do not require off-board computing or reliance onpotentially inaccurate or non-existent a priori maps.

Embodiments of the present invention may use localization methods bysampling range readings from scanning lasers and ultrasonic sensors andby reasoning probabilistically about where the robot is within itsinternal model of the world. The robot localization problem may bedivided into two sub-tasks: global position estimation and localposition tracking. Global position estimation is the ability todetermine the robot's position in an a priori or previously learned map,given no information other than that the robot is somewhere in theregion represented by the map. Once a robot's position has been found inthe map, local tracking is the problem of keeping track of the robot'sposition over time and movement.

The robot's state space may be enhanced by localizaton methods such asMonte Carlo techniques and Markovian probability grid approaches forposition estimation, as are well know by those of ordinary skill in theart. Many of these techniques provide efficient and substantiallyaccurate mobile robot localization.

With a substantially accurate position for the robot determined, localtracking can maintain the robot's position over time and movement usingdead-reckoning, additional global positioning estimation, orcombinations thereof. Dead-reckoning is a method of navigation bykeeping track of how far you have gone in any particular direction. Forexample, dead reckoning would determine that a robot has moved adistance of about five meters at an angle from the current pose of about37 degrees if the robot moves four meters forward, turns 90 degrees tothe right, and moves forward three meters. Dead-reckoning can lead tonavigation errors if the distance traveled in a given direction, or theangle through which a robot turns, is interpreted incorrectly. This canhappen, for example, if one or more of the wheels on the robot spin inplace when the robot encounters an obstacle.

Therefore, dead reckoning accuracy may be bolstered by sensorinformation from the environment, new global positioning estimates, orcombinations thereof. With some form of a map, the robot can use rangemeasurements to map features to enhance the accuracy of a pose estimate.Furthermore, the accuracy of a pose estimate may be enhanced by newrange measurements (e.g., laser scans) into a map that may be growing insize and accuracy. In Simultaneous Localization and Mapping (SLAM),information from the robot's encoders and laser sensors may berepresented as a network of probabilistic constraints linking thesuccessive positions (poses) of the robot. The encoders may relate onerobot pose to the next via dead-reckoning. To give further constraintsbetween robot poses, the laser scans may be matched with dead-reckoning,including constraints for when a robot returns to a previously-visitedarea.

The robot abstractions may include environmental occupancy gridattributes 240. One form of map that may be useful from both the robot'sperspective and an operator's perspective is an occupancy grid. Anenvironmental occupancy grid, formed by an occupancy grid abstraction240 is illustrated in FIG. 7. In forming an occupancy grid 250, a robotcoordinate system may be defined in Cartesian coordinates relative tothe robots orientation such that, for example, the X axis is to theright, the Y axis is straight ahead, and the Z-axis is up. Another robotcoordinate system may be cylindrical coordinates with a range, angle,and height relative to the robot current orientation. Furthermore,occupancy grids may be translated to other coordinate systems for use byan operator.

An occupancy grid map 390 may be developed by dividing the environmentinto a discrete grid of occupancy cells 395 and assigning a probabilityto each grid indicating whether the grid is occupied by an object.Initially, the occupancy grid may be set so that every occupancy cell isset to an initial probability. As the robot scans the environment, rangedata developed from the scans may be used to update the occupancy grid.For example, based on range data, the robot may detect an object at aspecific orientation and range away from the robot. This range data maybe converted to a different coordinate system (e.g., local or worldCartesian coordinates). As a result of this detection, the robot mayincrease the probability that the particular occupancy cell is occupiedand decrease the probability that occupancy cells between the robot andthe detected object are occupied. As the robot moves through itsenvironment, new horizons may be exposed to the robots sensors, whichenable the occupancy grid to be expanded and enhanced. To enhance mapbuilding and localization even further, multiple robots may explore anenvironment and cooperatively communicate their map information to eachother or a robot controller to cooperatively build a map of the area.

The example occupancy grid map 390 as it might be presented to anoperator is illustrated in FIG. 7. The grid cells 395 can be seen assmall squares on this occupancy grid 390. A robot path 380 is show toillustrate how the robot may have moved through the environment inconstructing the occupancy grid 390. Of course, those of ordinary skillin the art with recognize that, depending on the application andexpected environment, the occupancy grid 390 may be defined in anysuitable coordinate system and may vary in resolution (i.e., size ofeach occupancy cell). In addition, the occupancy grid 390 may include adynamic resolution such that the resolution may start out quite coarsewhile the robot discovers the environment, then evolve to a finerresolution as the robot becomes more familiar with its surroundings.

3. Robotic Intelligence Kernel

A robot platform 100 may include a robot intelligence kernel (may alsobe referred to herein as intelligence kernel), which coalesces hardwarecomponents for sensing, motion, manipulation, and actions with softwarecomponents for perception, communication, behavior, and world modelinginto a single cognitive behavior kernel that provides intrinsicintelligence for a wide variety of unmanned robot platforms. Theintelligence kernel architecture may be configured to support multiplelevels of robot autonomy that may be dynamically modified depending onoperating conditions and operator wishes.

The robot intelligence kernel (RIK) may be used for developing a varietyof intelligent robotic capabilities. By way of example, and notlimitation, some of these capabilities including visual pursuit,intruder detection and neutralization, security applications, urbanreconnaissance, search and rescue, remote contamination survey, andcountermine operations.

Referring back to the software architecture diagram of FIG. 3, the RIKcomprises a multi-level abstraction including a robot behavior level 250and a cognitive level 270. The RIK may also include the robotabstraction level 230 and the hardware abstraction level 210 discussedabove.

Above the robot abstraction level 230, the RIK includes the robotbehavior level 270, which define specific complex behaviors that arobot, or a robot operator, may want to accomplish. Each complex robotbehavior may utilize a variety of robot attributes, and in some cases avariety of hardware abstractions, to perform the specific robotbehavior.

Above the robot behavior level 250, the RIK includes the cognitive level270, which provides cognitive conduct modules to blend and orchestratethe asynchronous events from the complex robot behaviors and genericrobot behaviors into combinations of functions exhibiting cognitivebehaviors, wherein high level decision making may be performed by therobot, the operator, or combinations of the robot and the operator.

Some embodiments of the RIK may include, at the lowest level, thehardware abstraction level 210, which provides for portable, objectoriented access to low level hardware perception and control modulesthat may be present on a robot. These hardware abstractions have beendiscussed above in the discussion of the GRA.

Some embodiments of the RIK may include, above the hardware abstractionlevel 210, the robot abstraction level 230 including generic robotabstractions, which provide atomic elements (i.e., building blocks) ofgeneric robot attributes and develop a membrane between the low levelhardware abstractions and control based on generic robot functions. Eachgeneric robot abstraction may utilize a variety of hardware abstractionsto accomplish its individual function. These generic robot abstractionshave been discussed above in the discussion of the GRA.

3.1. Robot Behaviors

While the robot abstraction level 230 focuses on generic robotattributes, higher levels of the RIK may focus on; relatively complexrobot behaviors at the robot behavior level 250, or on robotintelligence and operator collaboration at the cognitive level 270.

The robot behavior level 250 includes generic robot classes comprisingfunctionality common to supporting behavior across most robot types. Forexample, the robot behavior level includes utility functions (e.g.,Calculate angle to goal) and data structures that apply acrosssubstantially all robot types (e.g., waypoint lists). At the same time,the robot behavior level defines the abstractions to be free fromimplementation specifics such that the robot behaviors are substantiallygeneric to all robots.

The robot behavior level 250, as illustrated in FIG. 8 robot, may beloosely separated into reactive behaviors 252 and deliberative behaviors254. Of course, it will be readily apparent to those of ordinary skillin the art that the modules shown in FIG. 8 are a representative, ratherthan comprehensive, example of robot behaviors.

The reactive behaviors 252 may be characterized as behaviors wherein therobot reacts to its perception of the environment based on robotattributes, hardware abstractions, or combinations thereof. Some ofthese reactive behaviors may include; autonomous navigation, obstacleavoidance, guarded motion, visual tracking, laser tracking, get-unstuckbehavior, and reactive planning. As examples, and not limitations,details regarding some of these behaviors are discussed in the sectionbelow regarding application specific behaviors.

In contrast, deliberative behaviors 254 may be characterized asbehaviors wherein the robot may need to make decisions on how to proceedbased on the results of the reactive behaviors, information from therobot attributes and hardware abstractions, or combinations thereof.Some of these deliberative behaviors may include; waypoint navigationwith automatic speed adjustment, global path planning, and occupancychange detection. As examples, and not limitations, details regardingsome of these behaviors are discussed in the section below regardingapplication specific behaviors.

3.2. Cognitive Conduct

The cognitive conduct level 270, as illustrated in FIG. 9, representsthe highest level of abstraction, wherein significant robot intelligencemay be built in to cognitive conduct modules, as well as significantoperator-robot collaboration to perform complex tasks requiring enhancedrobot initiative 299. Cognitive conduct modules blend and orchestrateasynchronous firings from the reactive behaviors 252, deliberativebehaviors 254, and robot attributes 230 into intelligent robot conduct.Cognitive conduct modules may include conduct such as GoTo 272, whereinthe operator may simply give a coordinate for the robot to go to and therobot takes the initiative to plan a path and get to the specifiedlocation. This GoTo conduct 272 may include a combination of robotbehaviors 250, robot attributes 230, and hardware abstractions 210, suchas, for example, obstacle avoidance, get-unstuck, reactive pathplanning, deliberative path planning, and waypoint navigation.

Another representative cognitive conduct module is human detection andpursuit 274, wherein the robot may react to changes in the environmentand pursue those changes. This detection and pursuit conduct 274 mayalso include pursuit of other objects, such as, for example, anotherrobot. The detection and pursuit 274 conduct may include a combinationof robot behaviors 250, robot attributes 230, and hardware abstractions210, such as, for example, occupancy change detection, laser tracking,visual tracking, deliberative path planning, reactive path planning, andobstacle avoidance.

Other representative cognitive conduct modules include conduct such asexploration and reconnaissance conduct 276 combined with map building,leader/follower conduct 278, and search and identify conduct 280.

Of course, it will be readily apparent to those of ordinary skill in theart that the cognitive conduct modules shown in FIG. 9 are arepresentative, rather than comprehensive example of robot conduct thatmay be implemented using embodiments if the present invention.

3.3. Timing and Behavior Adaptation

A notable aspect of the RIK is that the cognitive conduct modules 270and robot behaviors 250 generally operate from a perception of speed ofmotion in relationship to objects and obstacles. In other words, ratherthan being concerned with spatial horizons and the distance away from anobject, the cognitive conduct 270 and robot behaviors 250 are largelyconcerned with temporal horizons and how soon the robot may encounter anobject. This enables defining the cognitive conduct 270 and robotbehaviors 250 in a relativistic sense wherein, for example, the modulesinterpret motion as an event horizon wherein the robot may only beconcerned with obstacles inside the event horizon. For example, a robotbehavior 250 is not necessarily concerned with an object that is 10meters away. Rather, the robot behavior 250 may be concerned that it mayreach the object in two seconds. Thus, the object may be within theevent horizon when the object is 10 meters away and the robot is movingtoward it at 5 meters/sec, whereas if the object is 10 meters away andthe robot is moving at 2 meters/second, the object may not be within theevent horizon.

This relativistic perception enables an adaptation to processing powerand current task load. If the robot is very busy, for example processingvideo, it may need to reduce its frequency of processing each task. Inother words, the amount of time to loop through all the cognitiveconduct 270 and robot behaviors 250 may increase. However, with the RIK,the cognitive conduct 270 and robot behaviors 250 can adapt to thisdifference in frequency by modifying its behaviors. For example, if thetime through a loop reduces from 200 Hz to 100 Hz, the behaviors andconducts will know about this change in loop frequency and may modifythe way it makes a speed adjustment to avoid an object. For example, therobot may need a larger change in its speed of motion to account for thefact that the next opportunity to adjust the speed is twice more distantin the future at 100 Hz than it would be at 200 Hz. This becomes moreapparent in the discussion below, regarding the guarded motion behavior.

To enable and control this temporal awareness, the RIK includes a globaltiming loop in which cognitive conduct 270 and robot behaviors 250 mayoperate. Using this global timing loop, each module can be made aware ofinformation such as, for example, average time through a loop minimumand maximum time through a loop, and expected delay for next timingtick.

With this temporal awareness, the robot tends to modify its behavior byadjusting its motion, and motion of its manipulators, relative to itssurroundings rather than adjusting its position relative to a distanceto an object. Of course, with the wide array of perceptors, the robot isstill very much aware of its pose and position relative to itsenvironment and can modify its behavior based on this positionalawareness. However, with the RIK, the temporal awareness is generallymore influential on the cognitive conduct modules and robot behaviorsthan the positional awareness.

3.4. Dynamic Autonomy

To enhance the operator/robot tradeoff of control, the intelligencekernel provides a dynamic autonomy structure, which is a decompositionof autonomy levels, allowing methods for shared control to permeate alllevels of the multi-level abstraction. Furthermore, the intelligencekernel creates an object-oriented software architecture, which mayrequire little or no source code changes when ported to other platformsand low-level proprietary controllers.

The dynamic autonomy structure of the RIK provides a multi-levelharmonization between human intervention and robot initiative 299 acrossrobot behaviors. As capabilities and limitations change for both thehuman and the robot due to workload, operator expertise, communicationdropout, and other factors, the RIK architecture enables shifts from onelevel of autonomy to another. Consequently, the ability of the robot toprotect itself, make decisions, and accomplish tasks without humanassistance may enable increased operator efficiency.

FIGS. 10A and 10B are depictions of a representative embodiment of adynamic autonomy structure illustrating different levels of interactionbetween operator intervention 291 and robot initiative 299. As referredto herein operator, or operator intervention 291, may include humanoperation via a remote computer in communication with the robot, remoteoperation by some other form of artificial intelligence operating on aremote computer in communication with the robot, or some combinationthereof.

At the lowest level, referred to as teleoperation mode 293, the robotmay operate completely under remote control and take no initiative toperform operations on its own. At the second level, referred to as safemode 294, robot movement is dependent on manual control from a remoteoperator. However, in safe mode 294, the robot may be equipped with alevel of initiative that prevents the operator from causing the robot tocollide with obstacles. At the third level, referred to as shared mode295, the robot can relieve the operator from the burden of directcontrol. For example, the robot may use reactive navigation to find apath based on the robot's perception of the environment. Shared mode 295provides for a balanced allocation of roles and responsibilities. Therobot accepts varying levels of operator intervention 291 and maysupport dialogue through the use of scripted suggestions (e.g., “Pathblocked! Continue left or right?”) and other text messages that mayappear within a graphical interface. At the fourth level, referred to ascollaborative tasking mode 296, a high level of collaborative taskingmay be developed between the operator and the robot using a series ofhigh-level tasks such as patrol, search region or follow path. Incollaborative tasking mode 296, operator intervention 291 occurs on thetasking level, while the robot manages most decision-making andnavigation. At the highest level, referred to as autonomous mode 297, arobot may behave in a substantially autonomous manner, needing nothingmore than being enabled by an operator and perhaps given a very highlevel command such as, for example, survey the area, or search forhumans.

FIG. 10A illustrates a representative embodiment of how tasks may beallocated between the operator and the robot. For example, teleoperationmode 293 may be configured such that the operator defines tasks,supervises direction, motivates motion, and prevents collision, in sucha way that the robot takes no initiative and the operator maintainscontrol. In safe mode 294, the operator may still define tasks,supervise direction, and motivate motion, while allowing the robot totake the initiative to prevent collisions. In shared mode 295, theoperator may still define tasks and supervise direction, while allowingthe robot to motivate motion and prevent collisions. In collaborativetasking mode 296, the robot may possess the initiative to preventcollisions, motivate motion, and supervise direction, whilerelinquishing operator intervention 291 to define task goals. Inautonomous mode 297, the robot's initiative may prevent collisions,motivate motion, supervise direction, and define task goals. Of course,those of ordinary skill in the art will recognize that this allocationof tasks between the operator and the robot is a representativeallocation. Many other tasks and behaviors, and allocation of thosetasks and behaviors, are contemplated within the scope of the presentinvention.

FIG. 10B illustrates various cognitive conduct, robot behaviors, robotattributes, and hardware abstractions that may be available at differentlevels of robot autonomy. In general, moving from the teleoperation mode293 toward the autonomous mode 297 represents an increase in the amountof robot initiative 299 and a decrease in the amount of operatorintervention 291. Conversely, moving from the autonomous mode 297 towardthe teleoperation mode 293 represents a decrease in the amount of robotinitiative 299 and an increase in the amount of operator intervention291. Of course, those of ordinary skill in the art will recognize thatFIG. 10B is a representative sample of available conduct, behaviors,attributes, and hardware, as well as a representative allocation betweenautonomy levels. The RIK is configured such that many modules mayoperate across different levels of autonomy by modifying the amount ofoperator intervention 291, modifying the amount of robot initiative 299,or combinations thereof.

The autonomy levels are structured in the intelligence kernel such thateach new level of autonomy is built on, and encompasses, the subsequentlevel. For example, a guarded motion mode processing (explained morefully below) may includes the behavior and representational frameworkutilized by the teleoperation mode 293 processing, but also includeadditional levels of robot initiative 299 based on the various robotattributes (e.g., related to directional motion) created in response tothe teleoperation mode 293. Shared mode 295 may include all of thefunctionality and direct control of safe mode 294, but also allows robotinitiative 299 in response to the abstractions produced through theguarded motion mode processing (e.g., fused range abstractions createdin response to the direction motion abstractions). In addition, thecollaborative tasking mode 296 may initiate robot responses to theabstractions created in shared mode 295 processing such as recognitionthat a box canyon has been entered or that a communication link has beenlost.

For a robotic system to gracefully accept a full spectrum ofintervention possibilities, interaction issues cannot be handled merelyas augmentations to a control system. Therefore, opportunities foroperator intervention 291 and robot initiative 299 are incorporated asan integral part of the robot's intrinsic intelligence. Moreover, forautonomous capabilities to evolve, the RIK is configured such that arobot is able to recognize when help is needed from an operator, otherrobot, or combinations thereof and learn from these interactions.

As an example, in one representative embodiment, the robot includes aSony CCD camera that can pan, tilt and zoom to provide visual feedbackto the operator in the teleoperation mode 293. The robot may also usethis camera with increased robot initiative 299 to characterize theenvironment and even conduct object tracking.

In this embodiment, the RIK provides a graduated process for the robotto protect itself and the environment. To do so, the RIK may fuse avariety of range sensor information. A laser range finder may be mountedon the front, and sonar perceptors may be located around the mid-sectionof the robot. The robot also may include highly sensitive bump stripsaround its perimeter that register whether anything has been touched. Toprotect the top of the robot, especially the cameras andmission-specific sensors placed on top of the robot, infrared proximitysensors may be included to indicate when an object is less than a fewinches from the robot. Additional infrared proximity sensors may beplaced on the bottom of the robot and point ahead of the robot towardthe ground in order to prevent the robot from traveling into open space(e.g., traveling off of a landing down a stairway). Together, thesesensors provide a substantial field of protection around the robot andallow the operator to command the robot with increased confidence thatthe robot can take initiative to protect itself or its environment.

However, avoiding obstacles may be insufficient. Many adverseenvironments may include forms of uneven terrain such as rubble. Therobot should be able to recognize and respond to these obstacles.Inertial sensors may be used to provide acceleration data in threedimensions. This inertial information may be fused with information fromthe wheel encoders giving velocity and acceleration of the wheels, andelectrical current draw from the batteries, to produce a measure of“unexpected” resistance that may be encountered by the robot. As part ofthe dynamic autonomy, the operator may be able to choose to set aresistance limit that will automatically stop the robot once thespecified threshold has been exceeded. The resistance limit may beuseful not only for rough terrain, but also in situations when theoperator needs to override the “safe motion” capabilities (based on theobstacle avoidance sensors) to do things like push chairs and boxes outof the way and push doors open.

In addition, the RIK enables operators to collaborate with mobilerobots, by defining an appropriate level of discourse, including ashared vocabulary and a shared cognitive work space collaborativelyconstructed and updated on the fly through interaction with the realworld. This cognitive work space could consist of terrain overlaid withsemantic abstractions generated through autonomous recognition ofenvironmental features with point-and-click operator validation andiconographic insertion of map entities. Real-time semantic mapsconstructed collaboratively by humans, ground robots and air vehiclescould serve as the basis for a spectrum of mutual human-robotinteractions including tasking, situation awareness, human-assistedperception and collaborative environmental “understanding.” Thus, theRIK enables human-robot communication within the context of a missionbased on shared semantic maps between the robotic system and theoperator.

With reference to FIGS. 10A and 10B, additional details of the dynamicautonomy structure and corresponding operation modes can be discussed.

3.4.1. Teleoperation Mode

In teleoperation mode 293, the operator has full, continuous control ofthe robot at a low level. The robot takes little or no initiativeexcept, for example, to stop after a specified time if it recognizesthat communications have failed. Because the robot takes little or noinitiative in this mode, the dynamic autonomy implementation providesappropriate situation awareness to the operator using perceptual datafused from many different sensors. For example, a tilt sensor mayprovide data on whether the robot is in danger of overturning. Inertialeffects and abnormal torque on the wheels (i.e., forces not associatedwith acceleration) are fused to produce a measure of resistance as when,for example, the robot is climbing over or pushing against an obstacle.Even in teleoperation mode 293, the operator may be able to choose toactivate a resistance limit that permits the robot to respond to highresistance and bump sensors. Also, a specialized interface may providethe operator with abstracted auditory, graphical and textualrepresentations of the environment and task.

Some representative behaviors and attributes that may be defined forteleoperation mode 293 include joystick operation, perceptor status,power assessment, and system status.

3.4.2. Safe Mode

In safe mode 294, the operator directs movements of the robot, but therobot takes initiative to protect itself. In doing so, this mode freesthe operator to issue motion commands with less regard to protecting therobot, greatly accelerating the speed and confidence with which theoperator can accomplish remote tasks. The robot may assess its ownstatus and surrounding environment to decide whether commands are safe.For example, the robot possesses a substantial self-awareness of itsposition and will attempt to stop its motion before a collision, placingminimal limits on the operator. In addition, the robot may be configuredto notify the operator of environmental features (e.g., box canyon,corner, and hallway), immediate obstacles, tilt, resistance, etc. andalso continuously assesses the validity of its diverse sensor readingsand communication capabilities. In safe mode 294, the robot may beconfigured to refuse to undertake a task if it does not have the ability(i.e., sufficient power or perceptual resources) to safely accomplishit.

Some representative behaviors and attributes that may be defined forsafe mode 294 include guarded motion, resistance limits, and bumpsensing.

3.4.3. Shared Mode

In shared mode 295, the robot may take the initiative to choose its ownpath, responds autonomously to the environment, and work to accomplishlocal objectives. This initiative is primarily reactive rather thandeliberative. In terms of navigation, shared mode 295 may be configuredsuch that the robot responds only to its local (e.g., a two second eventhorizon or a six meter radius), sensed environment. Although the robotmay handle the low-level navigation and obstacle avoidance, the operatormay supply intermittent input, often at the robot's request, to guidethe robot in general directions. For example, a “Get Unstuck” behaviorenables the robot to autonomously extricate itself from highly clutteredareas that may be difficult for a remote operator to handle.

Some representative behaviors and attributes that may be defined forshared mode 295 include reactive planning, get unstuck behavior, andobstacle avoidance.

3.4.4. Colaberative Tasking Mode

In collaborative tasking mode 297, the robot may perform tasks such as,for example, global path planning to select its own route, requiring nooperator input except high-level tasking such as “follow that target” or“search this area” (perhaps specified by drawing a circle around a givenarea on the map created by the robot). For all these levels, theintelligence resides on the robot itself, such that off-board processingis unnecessary. To permit deployment within shielded structures, acustomized communication protocol enables very low bandwidthcommunications to pass over a serial radio link only when needed. Thesystem may use multiple and separate communications channels with theability to reroute data when one or more connection is lost.

Some representative cognitive conduct and robot behaviors, and robotattributes that may be defined for collaborative tasking mode 296include waypoint navigation, global path planning, go to behavior,retro-traverse behavior, area search behavior, and environment patrol.

3.4.5. Autonomous Mode

In autonomous mode 297, the robot may perform with minimal to nooperator intervention 291. For behaviors in autonomous mode 297, theoperator may simply give a command for the robot to perform. Other thanreporting status to the operator, the robot may be free to plan paths,prioritize tasks, and carry out the command using deliberative behaviorsdefined by the robots initiative.

Some representative behaviors and attributes that may be defined forautonomous mode 297 include pursuit behaviors, perimeter surveillance,urban reconnaissance, human presence detection, geological surveys,radiation surveys, virtual rail behavior, countermine operations, andseeking improvised explosive devices.

3.5. Rik Examples and Communication

Conventionally, robots have been designed as extensions of humanmobility and senses. Most seek to keep the human in substantiallycomplete control allowing the operator, through input from video camerasand other on-board sensors, to guide the robot and view remotelocations. In this conventional “master-slave” relationship, theoperator provides the intelligence and the robot is a mere mobileplatform to extend the operator's senses. The object is for theoperator, perched as it were on the robot's back, to complete somedesired tasks. As a result, conventional robot architectures may belimited by the need to maintain continuous, high-bandwidthcommunications links with their operators to supply clear, real-timevideo images and receive instructions. Operators may find it difficultto visually navigate when conditions are smoky, dusty, poorly lit,completely dark or full of obstacles and when communications are lostbecause of distance or obstructions.

The Robot Intelligence Kernel enables a modification to the way humansand robots interact, from master-slave to a collaborative relationshipin which the robot can assume varying degrees of autonomy. As the robotinitiative 299 increases, the operator can turn his or her attention tothe crucial tasks at hand (e.g., locating victims, hazards, dangerousmaterials; following suspects; measuring radiation and/or contaminantlevels) without worrying about moment-to-moment navigation decisions orcommunications gaps.

The RIK places the intelligence required for high levels of autonomywithin the robot. Unlike conventional designs, off-board processing isnot necessary. Furthermore, the RIK includes low bandwidth communicationprotocols and can adapt to changing connectivity and bandwidthcapabilities. By reducing or eliminating the need for high-bandwidthvideo feeds, the robot's real-world sensor information can be sent ascompact data packets over low-bandwidth (<1 Kbs) communication linkssuch as, for example, cell phone modems and long-range radio. The robotcontroller may then use these low bandwidth data packets to create acomprehensive graphical interface, similar to a computer game display,for monitoring and controlling the robot. Due to the low bandwidth needsenabled by the dynamic autonomy structure of the RIK, it may be possibleto maintain communications between the robot and the operator over manymiles and through thick concrete, canopy and even the ground itself.

FIG. 11 illustrates a representative embodiment of the RIK processing ofrobot abstractions 300 and communications operations 350 forcommunicating information about cognitive conduct, robot behaviors,robot attributes, and hardware abstractions to the robot controller orother robots. The upper portion 300 of FIG. 11 illustrates the robotabstractions, and hardware abstractions that may be fused to developrobot attributes. In the embodiment of FIG. 11, a differential GPS 302,a GPS 304, wheel encoders 306 and inertial data 312 comprise hardwareabstractions that may be processed by a Kalman filter 520. The robotattributes for mapping and localization 308 and localized pose 310 maybe developed by including information from, among other things, thewheel encoders 306 and inertial data 312. Furthermore, the localizedpose 310 may be a function of the results from mapping and localization308. As with the hardware abstractions, these robot attributes ofmapping and localization 308 and localized pose 310 may be processed bya Kalman filter 520.

Kalman filters 520 are efficient recursive filters that can estimate thestate of a dynamic system from a series of incomplete and noisymeasurements. By way of example, and not limitation, many of theperceptors used in the RIK include an emitter/sensor combination, suchas, for example, an acoustic emitter and a microphone array as asensors. These perceptors may exhibit different measurementcharacteristics depending on the relative pose of the emitter and targetand how they interact with the environment. In addition, to one degreeor another, the sensors may include noise characteristics relative tothe measured values. In robotic applications, Kalman filters 520 may beused in may applications for improving the information available fromperceptors. As one example of many applications, when tracking a target,information about the location, speed, and acceleration of the targetmay include significant corruption due to noise at any given instant oftime. However, in dynamic systems that include movement, a Kalman filter520 may exploit the dynamics of the target, which govern its timeprogression, to remove the effects of the noise and get a substantiallyaccurate estimate of the target's dynamics. Thus, a Kalman filter 520can use filtering to assist in estimating the targets location at thepresent time, as well as prediction to estimate a targets location at afuture time.

As a result of the Kalman filtering, or after being processed by theKalman filter 520, information from the hardware abstractions and robotattributes may be combined to develop other robot attributes. Asexamples, the robot attributes illustrated in FIG. 11, include position332, movement 334, obstruction 336, occupancy 338, and otherabstractions 340.

With the robot attributes developed, information from these robotattributes may be available for other modules within the RIK at thecognitive level 270, the robot behavior level 250, and the robotabstraction level 230.

In addition, information from these robot attributes may be processed bythe RIK and communicated to the robot controller or other robots, asillustrated by the lower portion of FIG. 11. Processing information fromthe robot conduct, behavior, and attributes, as well as information fromhardware abstractions serves to reduce the required bandwidth andlatency such that the proper information may be communicated quickly andconcisely. Processing steps performed by the RIK may include asignificance filter 352, a timing module, 354, prioritization 356, andbandwidth control 358.

The significance filter 352 may be used as a temporal filter to comparea time varying data stream from a given RIK module. By comparing currentdata to previous data, the current data may not need to sent at all ormay be compressed using conventional data compression techniques suchas, for example, run length encoding and Huffman encoding. Anotherexample would be imaging data, which may use data compression algorithmssuch as Joint Photographic Experts Group (JPEG) compression and MovingPicture Experts Group (MPEG) compression to significantly reduce theneeded bandwidth to communicate the information.

The timing module 354 may be used to monitor information from each RIKmodule to optimize the periodicity at which it may be needed. Someinformation may require periodic updates at a faster rate than others.In other words, timing modulation may be used to customize theperiodicity of transmissions of different types of information based onhow important it may be to receive high frequency updates for thatinformation. For example, it may be more important to notify anoperator, or other robot, of the robots position more often than itwould be to update the occupancy grid map.

The prioritization 356 operation may be used to determine whichinformation to send ahead of other information based on how important itmay be to minimize latency from when data is available to when it isreceived by an operator or another robot. For example, it may be moreimportant to reduce latency on control commands and control queriesrelative to map data. As another example, in some cognitive conductmodules where there may be significant collaboration between the robotand an operator, or in teleoperation mode where the operator is incontrol, it may be important to minimize the latency of videoinformation so that the operator does not perceive a significant timedelay between what the robot is perceiving and when it is presented tothe operator.

These examples illustrate that for prioritization 356, as well as thesignificance filter 352, the timing modulation 354, and the bandwidthcontrol 358, communication may be task dependent and autonomy modedependent. As a result, information that may be a high priority in oneautonomy mode may receive a lower priority in another autonomy mode.

The bandwidth control operation may be used to limit bandwidth based onthe communication channel's bandwidth and how much of that bandwidth maybe allocated to the robot. An example here might include progressiveJPEG wherein a less detailed (i.e., coarser) version of an image may betransmitted if limited bandwidth is available. For video, an example maybe to transmit at a lower frame rate.

After the communication processing is complete, the resultantinformation may be communicated to, or from, the robot controller, oranother robot. For example, the information may be sent from the robot'scommunication device 155, across the communication link 160, to acommunication device 185 on a robot controller, which includes amulti-robot interface 190.

FIGS. 12 and 13 illustrate a more general interaction between hardwareabstractions, robot abstractions, environment abstractions, robotbehaviors, and robot conduct. FIG. 12 illustrates general communicationbetween the hardware abstractions associated with sensor data servers210 (also referred to as hardware abstractions), the robot abstractions230 (also referred to as robot attributes), and environment abstractions239. Those of ordinary skill in the art will recognize that FIG. 12 isintended to show general interactions between abstractions in arepresentative embodiment and is not intended to show every interactionpossible within the GRA and RIK. Furthermore, it is not necessary todiscuss every line between every module. Some example interactions arediscussed to show general issues involved and describe some items fromFIG. 12 that may not be readily apparent from simply examining thedrawing. Generally, the robot abstractions 230 may receive and fuseinformation from a variety of sensor data servers 210. For example, informing a general abstraction about the robot's current movementattributes, the movement abstraction may include information from bumpsensors, GPS sensors, wheel encoders, compass sensors, gyroscopicsensors, tilt sensors, and the current brake state.

Some robot attributes 230, such as the mapping and localizationattribute 231 may use information from a variety of hardwareabstractions 210 as well as other robot attributes 230. The mapping andlocalization attribute 231 may use sonar and laser information fromhardware abstractions 210 together with position information and localposition information to assist in defining maps of the environment, andthe position of the robot on those maps. Line 360 is bold to indicatethat the mapping and localization attribute 231 may be used by any orall of the environment abstractions 239. For example, the occupancy gridabstraction uses information from the mapping and localization attribute231 to build an occupancy grid as is explained, among other places,above with respect to FIG. 7. Additionally, the robot map positionattribute may use the mapping and localization attribute 231 and theoccupancy grid attribute to determine the robot's current positionwithin the occupancy grid.

Bold line 362 indicates that any or all of the robot abstractions 230and environment abstractions 239 may be used at higher levels of the RIKsuch as the communications layer 350, explained above with respect toFIG. 11, and the behavior modulation 260, explained below with respectto FIG. 13.

FIG. 13 illustrates general communication between the robot abstractions230 and environment abstractions 239 with higher level robot behaviorsand cognitive conduct. As with FIG. 12, those of ordinary skill in theart will recognize that FIG. 13 is intended to show general interactionsbetween abstractions, behaviors, and conduct in a representativeembodiment and is not intended to show every interaction possible withinthe GRA and RIK. Furthermore, it is not necessary to discuss every linebetween every module. Some example interactions are discussed to showgeneral issues involved and describe some items from FIG. 13 that maynot be readily apparent from simply examining the drawing.

As an example, the event horizon attribute 362 may utilize and fuseinformation from robot abstractions 230 such as range and movement.Information from he event horizon attribute 362 may be used bybehaviors, such as, for example, the guarded motion behavior 500 and theobstacle avoidance behavior 600. Bold line 370 illustrates that theguarded motion behavior 500 and the obstacle avoidance behavior 600 maybe used by a variety of other robot behaviors and cognitive conduct,such as, for example, follow/pursuit conduct, virtual rail conduct,countermine conduct, area search behavior, and remote survey conduct.

4. Representative Behaviors and Conduct

The descriptions in this section illustrate representative embodimentsof robot behaviors and cognitive conduct that may be included inembodiments of the present invention. Of course, those of ordinary skillin the art will recognize these robot behaviors and cognitive conductare illustrative embodiments and are not intended to be a complete listor complete description of the robot behaviors and cognitive conductthat may be implemented in embodiments of the present invention.

In general, in the flow diagrams illustrated herein, T indicates anangular velocity of either the robot or a manipulator and V indicates alinear velocity. Also, generally, T and V are indicated as a percentageof a predetermined maximum. Thus V=20% indicates 20% of the presentlyspecified maximum velocity (which may be modified depending on thesituation) of the robot or manipulator. Similarly, T=20% indicates 20%of the presently specified maximum angular velocity of the robot ormanipulator. It will be understood that the presently specified maximumsmay be modified over time depending on the situations encountered. Inaddition, those of ordinary skill in the art will recognize that thevalues of linear and angular velocities used for the robot behaviors andcognitive conduct described herein are representative of a specificembodiment. While this specific embodiment may be useful in a widevariety of robot platform configurations, other linear and angularvelocities are contemplated within the scope of the present invention.

Furthermore, those of ordinary skill in the art will recognize that theuse of velocities, rather than absolute directions, is enabled largelyby the temporal awareness of the robot behaviors and cognitive conductin combination with the global timing loop. This gives the robotbehaviors and cognitive conduct an opportunity to adjust velocities oneach timing loop, enabling smoother accelerations and decelerations.Furthermore, the temporal awareness creates a behavior of constantlymoving toward a target in a relative sense, rather than attempting tomove toward an absolute spatial point.

4.1. Autonomous Navigation

Autonomous navigation may be a significant component for many mobileautonomous robot applications. Using autonomous navigation, a robot mayeffectively handle the task of traversing varied terrain whileresponding to positive and negative obstacles, uneven terrain, and otherhazards. Embodiments of the present invention enable the basicintelligence necessary to allow a broad range of robotic vehicles tonavigate effectively both indoors and outdoors.

Many proposed autonomous navigation systems simply provide GPS waypointnavigation. However, GPS can be jammed and may be unavailable indoors orunder forest canopy. A more autonomous navigation system includes theintrinsic intelligence to handle navigation even when externalassistance (including GPS and communications) has been lost. Embodimentsof the present invention include a portable, domain-general autonomousnavigation system which blends the responsiveness of reactive, sensorbased control with the cognitive approach found through waypointfollowing and path planning. Through its use of the perceptualabstractions within the robot attributes of the GRA, the autonomousnavigation system can be used with a diverse range of available sensors(e.g., range, inertial, attitude, bump) and available positioningsystems (e.g., GPS, laser, RF, etc.).

The autonomous navigation capability may scale automatically todifferent operational speeds, may be configured easily for differentperceptor suites and may be easily parameterized to be portable acrossdifferent robot geometries and locomotion devices. Two notable aspectsof autonomous navigation are a guarded motion behavior wherein the robotmay gracefully adjust its speed and direction near obstacles withoutneeding to come to a full stop and an obstacle avoidance behaviorwherein the robot may successfully navigate around known obstacles inits environment. Guarded motion and obstacle avoidance may work insynergy to create an autonomous navigation capability that adapts to therobots currently perceived environment. Moreover, the behavior structurethat governs autonomous navigation allows the entire assembly ofbehaviors to be used not only for obstacles but for other aspects of theenvironment which require careful maneuvering such as Landminedetection.

The robot's obstacle avoidance and navigation behaviors are derived froma number of robot attributes that enable the robot to avoid collisionsand find paths through dense obstacles. The reactive behaviors may beconfigured as nested decision trees comprising rules which “fire” basedon combinations of these perceptual abstractions.

The first level of behaviors, which may be referred to as actionprimitives, provide the basic capabilities important to most robotactivity. The behavior framework enables these primitives to be coupledand orchestrated to produce more complex navigational behaviors. Inother words, combining action primitives may involve switching from onebehavior to another, subsuming the outputs of another behavior orlayering multiple behaviors. For example, when encountering a densefield of obstacles that constrain motion in several directions, thestandard confluence of obstacle avoidance behaviors may give way to thehigh level navigational behavior “Get-Unstuck,” as is explained morefully below. This behavior involves rules which, when activated inresponse to combinations of perceptual abstractions, switch betweenseveral lower level behaviors including “Turn-till-head-is-clear” and“Backout.”

4.1.1. Guarded Motion Behavior

FIG. 14 is a software flow diagram illustrating components of analgorithm for the guarded motion behavior 500 according to embodimentsof the present invention. Guarded motion may fuse information from avariety of robot attributes and hardware abstractions, such as, forexample, motions attributes, range attributes, and bump abstractions.The guarded motion behavior 500 uses these attributes and abstractionsin each direction (i.e., front, left, right, and back) around the robotto determine the distance to obstacles in all directions around therobot.

The need for guarded motion has been well documented in the literatureregarding unmanned ground vehicles. A goal of guarded motion is for therobot to be able to drive at high speeds, either in response to theoperator or software directed control through one of the other robotbehaviors or cognitive conduct modules, while maintaining a safedistance between the vehicle and obstacles in its path. The conventionalapproach usually involves calculating this safe distance as a product ofthe robot's speed. However, this means that the deceleration and thedistance from the obstacle at which the robot will actually stop mayvary based on the low level controller responsiveness of the low levellocomotor controls and the physical attributes of the robot itself(e.g., wheels, weight, etc.). This variation in stopping speed anddistance may contribute to confusion on the part of the operator who mayperceive inconsistency in the behavior of the robot.

The guarded motion behavior according to embodiments of the presentinvention enables the robot to come to a stop at a substantiallyprecise, specified distance from an obstacle regardless of the robot'sinitial speed, its physical characteristics, and the responsiveness ofthe low-level locomotor control schema. As a result, the robot can takeinitiative to avoid collisions in a safe and consistent manner.

In general, the guarded motion behavior uses range sensing (e.g., fromlaser, sonar, infrared, or combinations thereof) of nearby obstacles toscale down its speed using an event horizon calculation. The eventhorizon determines the maximum speed the robot can safely travel andstill come to a stop, if needed, at a specified distance from theobstacle. By scaling down the speed by many small increments, perhapshundreds of times per second, it is possible to insure that regardlessof the commanded translational or rotational velocity, guarded motionwill stop the robot at substantially the same distance from an obstacle.As an example, if the robot is being driven near an obstacle rather thandirectly towards it, guarded motion will not stop the robot, but mayslow its speed according to the event horizon calculation. This improvesthe operator's ability to traverse cluttered areas and limits thepotential for operators to be frustrated by robot initiative.

The guarded motion algorithm is generally described for one direction,however, in actuality it is executed for each direction. In addition, itshould be emphasized that the process shown in FIG. 14 operates withinthe RIK framework of the global timing loop. Therefore, the guardedmotion behavior 500 is re-entered, and executes again, for each timingloop.

To begin, decision block 510 determines if guarded motion is enabled. Ifnot, control transitions to the end of the guarded motion behavior.

If guarded motion is enabled, control transfers to decision block 520 totest whether sensors indicate that the robot may have bumped into anobstacle. The robot may include tactile type sensors that detect contactwith obstacles. If these sensors are present, their hardwareabstractions may be queried to determine if they sense any contact. If abump is sensed, it is too late to perform guarded motion. As a result,operation block 525 causes the robot to move in a direction opposite tothe bump at a reduced speed that is 20% of a predefined maximum speedwithout turning, and then exits. This motion is indicated in operationblock 525 as no turn (i.e., T=0) and a speed in the opposite direction(i.e., V=−20%).

If no bump is detected, control transfers to decision block 530 where aresistance limit determination is performed. This resistance limitmeasures impedance to motion that may be incongruous with normalunimpeded motion. In this representative embodiment, the resistancelimit evaluates true if; the wheel acceleration equals zero, the forceon the wheels is greater than zero, the robot has an inertialacceleration that is less than 0.15, and the resulting impedance tomotion is greater than a predefined resistance limit. If this resistancelimit evaluation is true, operation block 535 halts motion in theimpeded direction, then exits. Of course, those of ordinary skill in theart will recognize that this is a specific implementation for anembodiment with wheels and a specific inertial acceleration threshold.Other embodiments, within the scope of the present invention, mayinclude different sensors and thresholds to determine if motion is beingimpeded in any given direction based on that embodiment's physicalconfiguration and method of locomotion.

If motion is not being impeded, control transfers to decision block 540to determine if any obstacles are within an event horizon. An eventhorizon is calculated as a predetermined temporal threshold plus a speedadjustment. In other words, obstacles inside of the event horizon areobstacles that the robot may collide with at the present speed anddirection. Once again, this calculation is performed in all directionsaround the robot. As a result, even if an obstacle is not directly inthe robots current path, which may include translational and rotationalmovement, it may be close enough to create a potential for a collision.As a result, the event horizon calculation may be used to decide whetherthe robot's current rotational and translational velocity will allow therobot time to stop before encroaching the predetermined thresholddistance. If there are no objects sensed within the event horizon, thereis no need to modify the robot's current motion and the algorithm exits.

If an obstacle is sensed within the event horizon, operation block 550begins a “safety glide” as part of the overall timing loop to reduce therobot's speed. As the robot's speed is reduced, the event horizon,proportional to that the speed, is reduced. If the reduction issufficient, the next time through the timing loop, the obstacle may nolonger be within the event horizon even though it may be closer to therobot. This combination of the event horizon and timing loop enablessmooth deceleration because each loop iteration where the event horizoncalculation exceeds the safety threshold, the speed of the robot (eithertranslational, rotational, or both) may be curtailed by a smallpercentage. This enables a smooth slow down and also enables the robotto proceed at the fastest speed that is safe. The new speed may bedetermined as a combination of the current speed and a loop speedadjustment. For example, and not limitation,New_speed=current_speed*(0.75-loop_speed_adjust). The loop_speed_adjustvariable may be modified to compensate for how often the timing loop isexecuted and the desired maximum rate of deceleration. Of course, thoseof ordinary skill in the art will recognize that this is a specificimplementation. While this implementation may encompass a large array ofrobot configurations, other embodiments within the scope of the presentinvention may include different scale factors for determining the newspeed based on a robot's tasks, locomotion methods, physical attributes,and the like.

Next, decision block 560 determines whether an obstacle is within adanger zone. This may include a spatial measurement wherein the range tothe obstacle in a given direction is less than a predeterminedthreshold. If not, there are likely no obstacles in the danger zone andthe process exits.

If an obstacle is detected in the danger zone, operation block 570 stopsmotion in the current direction and sets a flag indicating a motionobstruction, which may be used by other attributes, behaviors orconduct.

As mentioned earlier, the guarded motion behavior 500 operates on aglobal timing loop. Consequently, the guarded motion behavior 500 willbe re-entered and the process repeated on the next time tick of theglobal timing loop.

4.1.2. Obstacle Avoidance Behavior

FIG. 15 is a software flow diagram illustrating components of analgorithm for the obstacle voidance behavior that governs translationalvelocity 600 of the robot according to embodiments of the presentinvention. Similarly, FIG. 16 is a software flow diagram illustratingcomponents of an algorithm for the obstacle voidance behavior thatgoverns rotational velocity 650 of the robot. Obstacle avoidance mayfuse information from a variety of robot attributes and hardwareabstractions, such as, for example, motion attributes, range attributes,and bump abstractions. In addition, the obstacle avoidance behavior mayuse information from other robot behaviors such as, for example, theguarded motion behavior and a get unstuck behavior. The obstacleavoidance behavior uses these attributes, abstractions, and behaviors todetermine a translational velocity and a rotational velocity for therobot such that it can safely avoid known obstacles.

In general, the obstacle avoidance behavior uses range sensing (e.g.,from laser, sonar, infrared, or combinations thereof) of nearbyobstacles to adapt its translational velocity and rotation velocityusing the event horizon determinations explained earlier with respect tothe guarded motion behavior. As stated earlier, the obstacle avoidancebehavior works with the guarded motion behavior as building blocks forfull autonomous navigation. In addition, it should be emphasized thatthe processes shown in FIGS. 15 and 16 operate within the RIK frameworkof the global timing loop. Therefore, the obstacle avoidance behavior isre-entered, and executes again, for each timing loop.

To begin the translational velocity portion of FIG. 15, decision block602 determines if waypoint following is enabled. If so, controltransfers out of the obstacle avoidance behavior to a waypoint followingbehavior, which is explained form fully below.

If waypoint following is not enabled, control transfers to decisionblock 604 to first test to see if the robot is blocked directly infront. If so, control transfers to operation block 606 to set therobot's translational speed to zero. Then, control transfers out of thetranslational velocity behavior and into the rotational velocitybehavior so the robot can attempt to turn around the object. This testat decision block 604 checks for objects directly in front of the robot.To reiterate, the obstacle avoidance behavior, like most behaviors andconducts in the RIK, is temporally based. In other words, the robot ismost aware of its velocity and whether objects are within an eventhorizon related to time until it may encounter an object. In the case ofbeing blocked in front, the robot may not be able to gracefully slowdown through the guarded motion behavior. Perhaps because the objectsimply appeared in front of the robot, without an opportunity to followtypical slow down procedures that may be used if an object is within anevent horizon. For example, the object may be another robot or a humanthat has quickly moved in front of the robot so that the guarded motionbehavior has not had an opportunity to be effective.

If nothing is blocking the robot in front, decision block 608 tests tosee if a detection behavior is in progress. A detection behavior may bea behavior where the robot is using a sensor in an attempt to findsomething. For example, the countermine conduct is a detection behaviorthat is searching for landmines. In these types of detection behaviors,obstacle avoidance may want to approach much closer to objects, or maywant to approach objects with a much slower speed to allow time for thedetection function to operate. Thus, if a detection behavior is active,operation block 610 sets a desired speed variable based on detectionparameters that may be important. By way of example, and not limitation,in the case of the countermine conduct this desired speed may be set as:Desired_Speed=Max_passover_rate−(Scan_amplitude/Scan_Speed). In thiscountermine conduct example, the Max_passover_rate may indicate amaximum desired speed for passing over the landmine. This speed may bereduced by other factors. For example, the (Scan_amplitude/Scan_Speed)term reduces the desired speed based on a factor of how fast the minesensor sweeps an area. Thus, the Scan_amplitude term defines a term ofthe extent of the scan sweep and the Scan_Speed defines the rate atwhich the scan happens. For example, with a large Scan_amplitude and asmall Scan_Speed, the Desired_Speed will be reduced significantlyrelative to the Max_passover_rate to generate a slow speed forperforming the scan. While countermine conduct is used as an example ofa detection behavior, those of ordinary skill in the art will recognizethat embodiments of the present invention may include a wide variety ofdetection behaviors, such as, for example, radiation detection, chemicaldetection, and the like.

If a detection behavior is not in progress, decision block 612 tests tosee if a velocity limit is set. In some embodiments of the invention, itmay be possible for the operator to set a velocity limit that the robotshould not exceed, even if the robot believes it may be able to safelygo faster. For example, if the operator is performing a detailed visualsearch, the robot may be performing autonomous navigation, while theoperator is controlling a camera. The operator may wish to keep therobot going slow to have time to perform the visual search.

If a velocity limit is set, operation block 614 sets the desired speedvariable relative to the velocity limit. The equation illustrated inoperation block 614 is a representative equation that may be used. The0.1 term is a term used to ensure that the robot continues to make veryslow progress, which may be useful to many of the robot attributes,behaviors, and conduct. In this equation, the Speed_Factor term is anumber from one to ten, which may be set by other software modules, forexample the guarded motion behavior, to indicate a relative speed atwhich the robot should proceed. Thus, the desired speed is set as afractional amount (between zero and one in 0.1 increments) of theMax_Limit_Speed.

If a velocity limit is not set, operation block 616 sets the desiredspeed variable relative to the maximum speed set for the robot (i.e.,Max_Speed) with an equation similar to that for operation block 614except Max_Speed is used rather than Max_Limit_Speed.

After the desired speed variable is set by block 610, 614, or 616,decision block 618 tests to see if anything is within the event horizon.This test may be based on the robot's physical dimensions, includingprotrusions from the robot such as an arm, relative to the robot'scurrent speed. As an example using an arm extension, something insidethe event horizon may be determined by the equation:

Min_Front_Range<1.0+Arm_Extension+(1.75*Abs(Current_Velocity))

Where the Min_Front_Range indicates a range to an obstacle in front, 1.0is a safety factor, Arm_Extension indicates the distance beyond therobot that the arm currently extends, and Current_Velocity indicates therobot's current translational velocity.

If there is something detected within the event horizon, operation block620 sets the current speed based on the distance to the obstacle. Thus,the example equation in block 620 sets the speed based on the range tothe object less a Forward_Threshold set as a safety factor. With thisspeed, guarded motion has an opportunity to be effective and the speedmay be reduced further on the next iteration of the timing loop if theobject is still within the event horizon. After setting the speed,control transfers out of the translational velocity behavior, and intothe rotational velocity behavior.

If there is nothing detected within the event horizon, operation blocksets the speed to the desired speed variable that was set previously byoperation block 614, 616, or 618. After setting the speed, controltransfers out of the translational velocity behavior, and into therotational velocity behavior

To begin the rotation velocity portion of FIG. 16, decision block 602determines if waypoint following is enabled. If so, control transfersout of the obstacle avoidance behavior to a waypoint following behavior,which is explained form fully below.

If waypoint following is not enabled, control transfers to decisionblock 604 to first test to see if the robot is blocked directly infront. If so, control transfers to operation block 606 to set therobot's translational speed to zero. Then, control transfers out of thetranslational velocity behavior and into the rotational velocitybehavior so the robot can attempt to turn around the object. This testat decision block 604 checks for objects directly in front of the robot.To reiterate, the obstacle avoidance behavior, like most behaviors andconducts in the RIK, is temporally based. In other words, the robot ismost aware of its velocity and whether objects are within an eventhorizon related to time until it may encounter an object. In the case ofbeing blocked in front, the robot may not be able to gracefully slowdown through the guarded motion behavior. Perhaps because the objectsimply appeared in front of the robot, without an opportunity to followtypical slow down procedures that may be used if an object is within anevent horizon. For example, the object may be another robot or a humanthat has quickly moved in front of the robot so that the guarded motionbehavior has not had an opportunity to be effective.

If nothing is blocking the robot in front, decision block 608 tests tosee if a detection behavior is in progress. A detection behavior may bea behavior where the robot is using a sensor in an attempt to findsomething. For example, the countermine conduct is a detection behaviorthat is searching for landmines. In these types of detection behaviors,obstacle avoidance may want to approach much closer to objects, or maywant to approach objects with a much slower speed to allow time for thedetection function to operate. Thus, if a detection behavior is active,operation block 610 sets a desired speed variable based on detectionparameters that may be important. By way of example, and not limitation,in the case of the countermine conduct this desired speed may be set as:Desired_Speed=Max_passover_rate−(Scan_amplitude/Scan_Speed). In thiscountermine conduct example, the Max_passover_rate may indicate amaximum desired speed for passing over the landmine. This speed may bereduced by other factors. For example, the (Scan_amplitude/Scan_Speed)term reduces the desired speed based on a factor of how fast the minesensor sweeps an area. Thus, the Scan_amplitude term defines a term ofthe extent of the scan sweep and the Scan_Speed defines the rate atwhich the scan happens. For example, with a large Scan_amplitude and asmall Scan_Speed, the Desired_Speed will be reduced significantlyrelative to the Max_passover_rate to generate a slow speed forperforming the scan. While countermine conduct is used as an example ofa detection behavior, those of ordinary skill in the art will recognizethat embodiments of the present invention may include a wide variety ofdetection behaviors, such as, for example, radiation detection, chemicaldetection, ground penetrating radar, and the like.

If a detection behavior is not in progress, decision block 612 tests tosee if a velocity limit is set. In some embodiments of the invention, itmay be possible for the operator to set a velocity limit that the robotshould not exceed, even if the robot believes it may be able to safelygo faster. For example, if the operator is performing a detailed visualsearch, the robot may be performing autonomous navigation, while theoperator is controlling a camera. The operator may wish to keep therobot going slow to have time to perform the visual search.

If a velocity limit is set, operation block 614 sets the desired speedvariable relative to the velocity limit. The equation illustrated inoperation block 614 is a representative equation that may be used. The0.1 term is a term used to ensure that the robot continues to make veryslow progress, which may be useful to many of the robot attributes,behaviors, and conduct. In this equation, the Speed_Factor term is anumber from one to ten, which may be set by other software modules, forexample the guarded motion behavior, to indicate a relative speed atwhich the robot should proceed. Thus, the desired speed is set as afractional amount (between zero and one in 0.1 increments) of theMax_Limit_Speed.

If a velocity limit is not set, operation block 616 sets the desiredspeed variable relative to the maximum speed set for the robot i.e.,Max_Speed) with an equation similar to that for operation block 614except Max_Speed is used rather than Max_Limit_Speed.

After the desired speed variable is set by block 610, 614, or 616,decision block 618 tests to see if anything is within the event horizon.This test may be based on the robot's physical dimensions, includingprotrusions from the robot such as an arm, relative to the robot'scurrent speed. As an example using an arm extension, something insidethe event horizon may be determined by the equation:

Min_Front_Range<1.0+Arm_Extension+(1.75*Abs(Current_Velocity))

Where the Min_Front_Range indicates a range to an obstacle in front, 1.0is a safety factor, Arm_Extension indicates the distance beyond therobot that the arm currently extends, and Current_Velocity indicates therobot's current translational velocity.

If there is something detected within the event horizon, operation block620 sets the current speed based on the distance to the obstacle. Thus,the example equation in block 620 sets the speed based on the range tothe object less a Forward_Threshold set as a safety factor. With thisspeed, guarded motion has an opportunity to be effective and the speedmay be reduced further on the next iteration of the timing loop if theobject is still within the event horizon. After setting the speed,control transfers out of the translational velocity behavior, and intothe rotational velocity behavior.

If there is nothing detected within the event horizon, operation block622 sets the robot's current speed to the desired speed variable thatwas set previously by operation block 614, 616, or 618. After settingthe speed, control transfers out of the translational velocity behavior600, and into the rotational velocity behavior 650.

FIG. 16 illustrates a representative software flow diagram illustratingcomponents of an algorithm for the obstacle voidance behavior thatgoverns rotational velocity behavior 650 of the robot. To begin therotational velocity behavior of FIG. 16, decision block 652 determinesif waypoint following is enabled. If so, control transfers to decisionblock 654 to determine if the angle to a target exceeds a predefinedthreshold.

At decision block 658, the process checks to see if the robot is blockedin front. If so, the process performs a series of checks to see whereother obstacles may be to determine a desired rotational velocity anddirection. This obstacle checking process begins with decision block 660testing to see if the robot is blocked on the left side. If the robot isblocked on the left side, and also in front, operation block 662 sets anew value for a turn velocity to the right. In the representativeembodiment illustrated in FIG. 16 a positive rotational velocity isdefined as a turn to the left and a negative rotational velocity isdefined as a turn to the right. Thus, generally, Turn_left is a positivevalue indicating a rotational velocity to the left and Turn_right is anegative value indicating a rotational velocity to the right. Thus,operation block 662 reduces the rotational velocity in the currentdirection by about one half plus a small offset used to ensure that therotational velocity does not reach zero. After setting the new rotationvelocity, the process exits.

If the robot is not blocked on the left, decision block 664 tests to seeif the robot in blocked on the right. If so, operation block 666 sets anew value for a turn velocity to the right similar to that velocitysetting in operation block 662. In other words, set the rotationalvelocity to the left to about one half plus a small offset used toensure that the rotational velocity does not reach zero. After settingthe new rotation velocity, the process exits.

If the robot is blocked in the front, but not on the left or right, theprocess then decides which way to turn to get around the blockage bychecking to see whether the nearest obstacle in a measurable range is tothe right or left and adjusting the rotational velocity to be away fromthe obstacle. Operation block 668 checks to see if the nearest obstacleis to the left. If so, operation block 670 sets the rotational velocityto the right (i.e., away from the obstacle) at a velocity of 30% of amaximum defined rotational velocity. If the nearest obstacle is not tothe left, operation block 672 sets the rotational velocity to the leftat a velocity of 30% of a maximum defined rotational velocity. Aftersetting the new rotation velocity by either operation block 670 or 672,the process exits.

If the robot was not blocked in front, based on decision block 658, thendecision block 674 performs a “threading the needle process.” Thisstarts with decision block 674 determining a range to obstacles that maystill be in front of the robot but not directly blocking the robot. Todo this, decision block 674 test to see if Min_Front_Range is greaterthan two times a predefined threshold for the front direction, and tosee if Min Narrow_Front is greater than two times the robot's length. Ifboth these tests are true, it may be relatively clear in front and theprocess decides to reduce the rotational velocity in the currentdirection to make the direction more straight ahead until the nextglobal timing loop. Therefore, decision block 676 tests to see if thecurrent rotational direction is left. If so, decision block 678 tests tosee if the magnitude of the left rotational velocity is greater thantwice a turn threshold. If so, operation block 680 reduces therotational velocity in the left direction by one half, and the processexits. If the current rotational direction is not left, decision block682 tests to see if the magnitude of the right rotational velocity isgreater than twice a turn threshold. If so, operation block 684 reducesthe rotational velocity in the right direction by one half, and theprocess exits.

If decision block 674, 678, or 682 evaluates false, decision block teststo see if anything is currently within the event horizon.

This test may be based on the robot's physical dimensions, includingprotrusions from the robot such as an arm, relative to the robot'scurrent speed. In addition, this test is likely the same as the eventhorizon described above for the translational velocity when discussingdecision block 618 on FIG. 15. In other words, is theMinimum_Front_Range less that an Event_Range. Wherein theEvent_Range=1.0+Arm_Extension+(1.75*Abs(Current Velocity)).

If there is nothing within the event horizon (i.e., decision block 690evaluates false), there is likely no need to change the currentrotational velocity so the process exits. If there is something withinthe event horizon, but not within the threading the needle process orblocking the robot in front, the rotational velocity may be adjusted ata more gradual rate. Thus, if operation block 690 evaluates true,decision block 692 tests to see if the closest object is on the leftside. If so, operation block 694 sets a new rotation velocity to theright. If the closest object is not on the left, operation block 696sets a new rotation velocity to the left. The rotational velocity thatis set in operation blocks 694 and 696 are similar except for direction.In this representative embodiment, the rotational velocity may be set asa function of the Event Range from the event horizon test of decisionblock 690. Thus, the rotational velocity may be set as:

(Event_Range−Min_Front_Range)/4.

After setting the rotational velocity in either operation block 694 or696, the process exits.

As mentioned earlier, the obstacle avoidance behavior 600 operates onthe global timing loop. Consequently, both the translational velocityand rotational velocity may be adjusted again on the next time tick ofthe global timing loop, allowing for relatively quick periodicadjustments to the velocities.

4.2. Get Unstuck Behavior

A get unstuck behavior 700, as illustrated in FIG. 17, includessignificant robot initiative to extricate itself from the stuck positionwith little or no help from the operator. Sometimes, when a robot isoperating under its own initiative, or even under operator control, therobot may get stuck and have difficulty getting free from that position.Often times, the operator may have limited understanding of the robotsposition relative to the robots understanding with its wide variety ofperceptors. In general, the get unstuck behavior 700 may use rangesensing (e.g., from laser, sonar, infrared, or combinations thereof) todetermine nearby obstacles and their position relative to the robot.

The get unstuck behavior 700 begins at decision block 710 by determiningif the current path is blocked. This blocked situation may be defined asobstacle present in front, on the front-right side, and on thefront-left side. If the path is blocked, control transfers to operationblock 740, which is explained below. For an example, and using the rangedefinitions defined above under the description of the range attribute,a blocked path may be defined by the Boolean equation:

    Blocked = ((right_in_front < (robot->forward_thresh + 0.2)) ||FRONT_BLOCKED) && (1_front < (robot->forward_thresh * 2)) && (r_front <(robot->forward_thresh * 2)) && (left_front < (robot->forward_thresh *2)) && (right_front < (robot->forward_thresh * 2))

Wherein: (robot->forward_thresh) is a predetermined threshold parameter,that may be robot specific, to define a safety distance, ormaneuverability distance, away from the robot.

If the path is not blocked, decision block 720 determines if forwardmotion and turning motion is obstructed. If motion is obstructed,control transfers to operation block 740, which is explained below. Foran example, this motion obstruction may be determined by the Booleanequation:

    Obstructed_motion = (FR_LEFT_BLOCKED || R_RIGHT_BLOCKED) &&(FR_RIGHT_BLOCKED || R_LEFT_BLOCKED) && FRONT_BLOCKED

If motion is not obstructed, decision block 730 determines if the robotis in a box canyon. If the robot is not in a box canyon, the get unstuckbehavior exits because it appears the robot is not in a stuck situation.If the robot is in a box canyon, control transfers to operation block740. For an example, this box canyon situation may be defined by theBoolean equation:

    Box_canyon = (right_in_front < (robot->forward_thresh+.2)) &&(right_front < (robot->forward_thresh * 2.0)) && (left_front <(robot->forward_thresh * 2.0)) && ((right_side + left_side) <(robot->turn_thresh * 3.0)) && (BACK_BLOCKED=0)

Wherein: (robot->turn_thresh) is a predetermined threshold parameter,which may be robot specific, to define a maneuverability distance thatenables the robot to turn around.

Once the determination has been made that the robot may be stuck,operation block 740 begins the process of attempting to get unstuck.Operation block 740 performs a back-out behavior. This back-out behaviorcauses the robot to backup from its present position while following thecontours of obstacles near the rear sides of the robot. In general, theback-out behavior uses range sensing (e.g., from laser, sonar, infrared,or combinations thereof) of nearby obstacles near the rear sides todetermine distance to the obstacles and provide assistance in followingthe contours of the obstacles. However, the back-out behavior may alsoinclude many robot attributes, including perception, position, boundingshape, and motion, to enable the robot to turn and back up whilecontinuously responding to nearby obstacles. Using this fusion ofattributes, the back-out behavior doesn't merely back the robot up, butrather allows the robot to closely follow the contours of whateverobstacles are around the robot.

For example movements, the robot may attempt to equalize the distancebetween obstacles on both sides, keep a substantially fixed distancefrom obstacles on the right side, or keep a substantially fixed distancebetween obstacles on the right side. As the back-out behaviorprogresses, decision block 750 determines if there is sufficient spaceon a side to perform a maneuver other than backing out. If there is notsufficient spaces, control transfers back to operation block 740 tocontinue the back-out behavior. If there is sufficient space on a side,control transfers to operation block 760. As an example, the sufficientspace on a side decision may be defined by the Boolean equation:

    Space_on_side = space_on_left || space_on_right, wherein:    Space_on_left = (1_front > (robot->forward_thresh+.2)) &&(turn_left > (robot->arm_length + robot->turn_thresh + .2)) &&(turn_left >= turn_right)     Space_on_right = (r_front >(robot->forward_thresh+.2)) && (turn_right > (robot->arm_length +robot->turn_thresh + .2)) && (turn_right >= turn_left))

Once sufficient space has been perceived on the right or left, operationblock 760 performs a turn-until-head-is-clear behavior. This behaviorcauses the robot to rotate in the sufficient space direction whileavoiding obstacles on the front sides. As the turn-until-head-is-clearbehavior progresses, decision block 770 determines if, and when, thehead is actually clear. If the head is not clear, control transfers backto the operation block 760 to continue the turn-until-head-is-clearbehavior. If the head is clear, control transfers to operation block760.

Once the head is clear, decision block 780 determines whether anacceptable egress route has been found. This egress route may be definedas an acceptable window of open space that exists for the robot to moveforward. To avoid potential cyclical behavior, the acceptable window maybe adjusted such that the robot does not head back toward the blockedpath or box canyon. If an acceptable egress route has not been found,control transfers back to operation block 740 to attempt the back-outbehavior again. If an acceptable egress route is found, the unstuckbehavior exits. As a specific example, the window may be defined by theequation:

window=1.25 meters−(seconds-in-behavior/10.0); and the egress route maybe defined as true if the window<(robot->forward_thresh*2.5).

As with the guarded motion behavior, the get-unstuck behavior 700operates on a global timing loop. Consequently, the get-unstuck behavior700 will be re-entered and the process repeated on the next time tick.

4.3. Real-Time Occupancy Change Analysis

FIG. 18 is a software flow diagram illustrating representativecomponents of an algorithm for performing a real-time occupancy changeanalysis behavior 800. Despite the much discussed potential for robotsto play a critical role in security applications, the reality is thatmany human presence and motion tracking techniques require that thesensor used in tracking be stationary, removing the possibility forplacement on a mobile robot platform. In addition, there is a need todetermine substantially accurate positions for changes to recognizedenvironmental features within a map. In other words, it may not beenough to know that something has moved or even the direction ofmovement. For effective change detection, a system should provide asubstantially accurate position of the new location.

The Real-Time Occupancy Change Analyzer (ROCA) algorithm compares thestate of the environment to its understanding of the world and reportsto an operator, or supporting robotic sensor, the position of and thevector to any change in the environment. The ROCA robot behavior 800includes laser-based tracking and positioning capability which enablesthe robot to precisely locate and track static and mobile features ofthe environment using a change detection algorithm that continuouslycompares current laser scans to an occupancy grid map. Depending on thelaser's range, the ROCA system may be used to detect changes up to 80meters from the current position of the laser range finder. Theoccupancy grid may be given a priori by an operator, built on the fly bythe robot as it moves through its environment, or built by a combinationof robot and operator collaboration. Changes in the occupancy grid maybe reported in near real-time to support a number of trackingcapabilities, such as camera tracking or a robotic follow capabilitywherein one or more robots are sent to the map location of the mostrecent change. Yet another possible use for the ROCA behavior is fortarget acquisition.

A notable aspect of the ROCA behavior is that rather than only providinga vector to the detected change, it provides the actual X, Y position ofthe change. Furthermore, the ROCA behavior can operate “on-the-move”meaning that unlike most human presence detection systems which must bestationary to work properly, it can detect changes in the features ofthe environment around it apart from of its own motion. This positionidentification and on-the-move capability enable tracking systems topredict future movement of the target and effectively search for atarget even if it becomes occluded.

In general, once the robot has identified a change, the change may beprocessed by several algorithms to filter the change data to removenoise and cluster the possible changes. Of the clustered changesidentified, the largest continuous cluster of detected changes (i.e.,“hits”) may be defined as locations of a change (e.g., possibleintruder) within either the global coordinate space, as a vector fromthe current pose of the robot, other useful coordinate systems, orcombinations thereof. This information then may be communicated to otherrobot attributes, robot behaviors, and cognitive conduct within the RIKas well as to other robots or an operator on a remote system.

As discussed earlier when discussing the range attribute, a variety ofcoordinate systems may be in use by the robot and an operator. By way ofexample, a local coordinate system may be defined by an operatorrelative to a space of interest (e.g., a building) or a world coordinatesystem defined by sensors such as a GPS unit, an iGPS unit, a compass,an altimeter, and the like. A robot coordinate system may be defined inCartesian coordinates relative to the robots orientation such that, forexample, the X axis is to the right, the Y axis is straight ahead, andthe Z-axis is up. Another robot coordinate system may be cylindricalcoordinates with a range, angle, and height relative to the robotcurrent orientation.

The software flow diagram shown in FIG. 18 includes representativecomponents of an algorithm for performing the ROCA behavior 800. Asstated earlier, the ROCA process 800 assumes that at least some form ofoccupancy grid has been established. However, due to the global timingloop execution model, details, probabilities, and new frontiers of theoccupancy grid may be built in parallel with the ROCA process 800. TheROCA process 800 begins at decision block 810 by testing to determine ifthe robot includes lasers, the laser data is valid, an occupancy grid isavailable, and the ROCA process is enabled. If not, the ROCA process 800ends.

If decision block 810 evaluates true, process block 820 performs a newlaser scan, which includes obtaining a raw laser scan, calculating worldcoordinates for data included in the raw laser scan, and converting theworld coordinates to the current occupancy grid. The raw laser scanincludes an array of data points from one or more lasers sweeps withrange data to objects encountered by the laser scan at various pointsalong the laser sweep. Using the present occupancy grid and presentrobot pose, the array of range data may be converted to an occupancygrid (referred to as laser-return occupancy grid) similar to the presentoccupancy grid map.

Next, decision block 830 tests to see if the current element of thearray of range data shows an occupancy element that is the same as theoccupancy element for the occupancy grid map. If so, control passes todecision block 860 at the bottom of the range data processing loop,which is discussed later.

If there is a difference between the laser-return occupancy cell and thecorresponding cell for the occupancy grid map, decision block 840 teststhe laser-return occupancy cell to see if it is part of an existingchange occurrence. In other words, if this cell is adjacent to anothercell that was flagged as containing a change, it may be part of the samechange. This may occur, for example, for an intruder, that is largeenough to be present in more than one occupancy grid. Of course, thistest may vary depending on, for example, the granularity of theoccupancy grid, accuracy of the laser scans, and size of the objects ofconcern. If decision block 840 evaluates true, operation block 842clusters this presently evaluated change with other change occurrencesthat may be adjacent to this change. Then control will transfer tooperation block 848.

If decision block 840 evaluates false, the presently evaluated change islikely due to a new change from a different object. As a result,operation block 844 increments a change occurrence counter to indicatethat there may be an additional change in the occupancy grid.

Operation block 848 records the current change occurrences and changeclusters whether from and existing cluster or a new cluster, thencontrol transfers to decision block 850.

Decision block 850 tests to see if the change occurrence counter isstill below a predetermined threshold. If there are a large number ofchanges, the changes may be due to inaccuracies in the robot's currentpose estimate. For example, if the pose estimate indicates that therobot has turned two degrees to the left, but in reality, the robot hasturned five degrees to the left, there may be a large number ofdifferences between the laser-return occupancy grid and the occupancygrid map. These large differences may be caused by the inaccuracies inthe pose estimate, which would cause inaccuracies in the conversion ofthe laser scans to the laser-return occupancy grid. In other words, skewin the alignment of the laser scan onto the occupancy grid map due toerrors in the robot's pose estimation, from rotation or translation, maycause a large number of differences. If this is the case, controltransfers to operation block 880 to update the position abstraction inan attempt to get a more accurate pose estimate. After receiving a newpose estimate from the position abstraction, the ROCA process beginsagain at decision block 810.

If decision block 850 evaluates true or decision block 860 was enteredfrom decision block 830, decision block 860 test to see if there aremore data points in the laser scan to process. If so, control transfersback to decision block 830 to process the next element in the laser scanarray.

If decision block 850 evaluates false, all the data in the laser scanarray has been processed and decision block 870 again tests to see ifthe change occurrence counter is still below a predetermined threshold.As discussed earlier, if the change occurrence counter is not below thepredetermined threshold, operation block 880 updates the positionabstraction in an attempt to get a more accurate pose estimate, the ROCAprocess begins again at decision block 810.

If decision block 870 evaluates true, then processing for this laserscan is complete and operation block 890 updates a change vector andinformation regarding change occurrences and change clusters is madeavailable to other robot attributes, robot behaviors, and cognitiveconduct modules.

By way of example, and not limitation, the ROCA results may be sent tothe user interface, used by a tracking behavior, and combinationsthereof. For example, ROCA results may be used with additional geometriccalculations to pan a visual camera, a thermal camera, or combinationthereof to fixate on one or more of the identified changes. Similarly, amanipulator, such as, for example, a weapon may be panned to acquire atarget identified as one of the changes. If the detected change ismoving, tracking position updates may arrive in near real time (theactual rate may depend on the speed and latency of the communicationchannel), allowing various sensors to continuously track the target. Ifdesired, the robot may also continuously move to the new locationidentified by the change detection system to provide a mobile trackingcapability.

When coupled with an operator interface, the tracked entity's movementsmay be indicated to an operator in near real time and visual data from acamera can be used by the operator to identify the tracked entity.

As with other behaviors, the ROCA behavior 800 operates on the globaltiming loop. Consequently, the ROCA behavior 800 will be re-entered andthe process repeated on the next time tick.

4.4. Virtual Rail Conduct

One representative cognitive conduct module enabled by the RIK and GRAis a virtual rail system for robots. Many industrial and researchapplications involve moving a vehicle or target at varying speeds alonga designated path. There is a need to follow physical paths repeatablyeither for purposes of transport, security applications or in order toaccurately record and analyze information such as component wear andtear (e.g., automotive testing), sensor responsiveness (e.g., sensorcharacterization), or environmental data (e.g., monitoring). Suchapplications require both accuracy and repeatability.

Conventional practice methods have required the building of physical oractual tracks along which a vehicle can be moved. Drawbacks of such anapproach include the significant limitations of the configuration ofpaths that may be created and the feasibility of building permanenttracks. Also, for characterization and other readily modifiable tasks,reconfiguration of physical track networks quickly becomes cost and timeprohibitive.

Although it has long been known that physical tracks or rails areproblematic, mobile robots have not had a means by which to maintainaccurate positioning apart from such fixed-track methods. For sometasks, absolute positioning can be achieved by various instrumentedsolutions such as visual, laser-based tracking systems or radiofrequency positioning systems that triangulate distance based on beaconsplaced in the environment. Each of these systems is costly to implement;in fact, the cost for purchasing and installing such a positioningsystem is often more than the total cost of the robot itself.

Moreover, the utility of visual or laser tracking systems is limited byocclusions within the environment. For example, RF beacons are onlyappropriate for environments where the beacons can be fixed in a static,known location. The physical properties of a remote sensing environmentare constantly changing. In fact, walls are often shifted within thebuilding to model different operational environments. Accordingly,absolute positioning is sometimes less feasible, impractical andfrequently impossible to implement. Therefore, there is a need toprovide a method and system for configuring a virtual track or railsystem for use by a robot.

The present invention includes various embodiments including a robotsystem configured to follow pre-planned routes forming a “virtual rail”or “virtual track” and may include defined speeds for traversing varioussegments of the pre-planned routes. One application of a virtual railsystem includes the repeated testing of a sensor or system tocharacterize the device. Due to the accuracy and repeatability of thevirtual rail system, sensors and systems may be tested with datacollected that conforms to a “sufficient comparable data” standard. Sucha data collection standard requires acceptance of data only whenconsistent and comparable data is generated in response to repeatabletests carried out under the same conditions. For example, the virtualrail system may be used in a laboratory, research facility, ormanufacturing environment to characterize a vast number of sensors.Accordingly, characterization tests that may previously have required asignificant amount of time for execution, may now be characterized in afraction of the time.

Sensor characterization is only one example of a specific application.Other applications include automated mail carts and other deliverysystems, security and surveillance systems, manufacturing and monitoringsystems. In particular, the technology is useful for parts handling, aswell as replacement of current railed robotic systems especially withinthe manufacturing and defense industries.

FIG. 19 is a block diagram of a robot system for implementing a virtualtrack for a robot, in accordance with an embodiment of the presentinvention. A robot system 2100 includes a robot 2102 and a controlgeneration system 2104. In robot system 2100, a user interfaces withcontrol generation system 2104 to implement a virtual track for tracingor following by robot 2102. Robot 2102 is responsive to programmingcommands generated by control generation system 2104 and further conveysfeedback and sensor information to control generation system 2104 overcommunication interface 2106. A user through a user interface of controlgeneration system 2104 designates a desired path comprised of one ormore representative path segments. In the various embodiments of thepresent invention, robot 2102 is configured or programmed to follow avirtual track or a virtual rail similar in resulting operation to arobot following a fixed track or physical rail. In the variousembodiments of the present invention, however, the shortcomings of afixed physical rail configuration are overcome by enabling the formationof a virtual track or rail system without the appreciated physical andeconomical limitations associated therewith.

FIG. 20 illustrates a user interface for generating a desired pathrepresentative of a virtual track or virtual rail, in accordance with anembodiment of the present invention. A user interface 2120 operating ona conventional computer or other hosting interface provides anenvironment wherein a user may configure and readily reconfigure avirtual track or rail configuration for execution and following by arobot.

The user interface 2120 provides an environment for the generation of adesired path comprised of at least one segment representative of thevirtual track for the robot. The user interface 2120 may take the formof a Computer Aided Design (CAD) program for the formation of thedesired path. The desired path comprised of one or more segmentsrepresentative of the virtual track for the robot may take the form oflines, arcs or any of a number of design shapes known by those ofordinary skill in the art, and are collectively referred to herein as“segments.” By way of example, a desired path 2122 includes a pluralityof line segments 2124-2132 with line segment 2132 illustrated as beingselected. Line segments 2124-2132 may be generated using any of a numberof commercially available CAD system that may generate file formats thatare readily convertible and parsable. By way of example and notlimitation, the CAD file format may be directly saved or converted intoa file format such as Drawing Exchange Format (.dxf).

FIG. 21 is a process diagram for configuring the desired path into awaypoint file for implementing a virtual track or rail and for executionby a robot, in accordance with an embodiment of the present invention. Avirtual track or rail is specified in the form of a desired path 2122(FIG. 20) including at least one segment representative of the virtualtrack as input through the user interface 2120 (FIG. 20). The graphicalinput of the desired path is converted or stored in a form that iscapable of further processing or manipulation by the control generationsystem 2104 (FIG. 19) which generates programming commands destined forexecution by robot 2102 (FIG. 19). By way of example and not limitation,the stored format for the desired path of the one or more segmentsrepresentative of the virtual track may be a drawing file 2202. Theformat of drawing file 2202, among others, includes file formats (e.g.,.dxf) configured to represent various line segments, arcs and otherdrawing elements as expressed by a user through a graphical userinterface 2120 (FIG. 20).

A path plan process 2204 receives the CAD-generated drawing file 2202and processes the one or more segments of the desired path into awaypoint file 2206 that includes instructions that are capable of beingexecuted by robot 2102 (FIG. 19). The processing of drawing file 2202includes the assignment process 2208 of input velocities 2200 to thesegments or vertices of the desired path segments or elements. Averification process 2210 analyzes the desired input velocities 2200 bycomparing the velocities with the mobility capabilities of robot 2102(FIG. 19). Discrepancies or incompatibilities between the desired pathand input velocities as compared with the execution capabilities ofrobot 2102 are reported and/or resolved.

Path plan process 2204 further includes a waypoint generation process2212 for generating waypoint file 2206 which precipitate from theoriginal drawing file 2202 undergoing assignment process 2208 followedby verification process 2210 for determining the compatibilities of thedesired path and the robot capabilities. Waypoint file 2206 includes alisting of waypoints as well as any modified velocities 2214 which maybe different than the originally specified input velocities 2200.

FIG. 22 illustrates a user interface for further processing the desiredpath into a program for execution by a robot, in accordance with anembodiment of the present invention. A path plan process user interface2420 provides an environment for the rendering of a previously defineddrawing file 2202 (FIG. 21) and further enables the generation of awaypoint file 2206 (FIG. 21) through the assignment of start and endpoints 2440, 2442 to the desired path 2422 as well as the association ofvelocities with such paths. The desired path 2422, comprised of one ormore segments 2424-2432 representative of the virtual track for therobot, thereafter includes assigned motion qualities and characteristicsincluding start and end points 2440, 2442 to the desired path 2442 aswell as assigned input velocities 2200 (FIG. 21) or speeds that shouldbe executed by robot 2102.

As stated, the one or more line segments 2424-2432 with the assignedmotion qualities is compared or verified through verification process2210 (FIG. 21) with the performance capabilities of a specific robot2102 which compares the requested desired path with mobility limitationsand capabilities of robot 2102. In one embodiment of the presentinvention, an algorithm analyzes the path including traversal of thesegments at various speeds, including velocity transitions between lineand arc segments and determines the turn gain to insure minimaloscillations during traversal of the line segments. Furthermore, thealgorithm is capable of carving smooth arcs by adjusting the turn gainbased on an analysis of the arc shape and the commanded forwardvelocity. This algorithm provides the ability to arbitrate betweenwaypoint following and motor schema control as speed and point typeschange.

After resolution of any inconsistencies or incompatibilities, a waypointfile 2206 (FIG. 21) is generated by path plan process 2204 with waypointfile 2206 (FIG. 21) being transferred over communication interface 2106(FIG. 19) to robot 2102 (FIG. 19) for execution. Robot 2102, executingthe various waypoints and specified velocities 2214 (FIG. 21) associatedtherewith, traces out or follows a virtual track or virtual rail asspecified and/or modified by a user through the control generationsystem 2104 (FIG. 19).

The user interface 2420 for controlling path plan process 2204 (FIG. 21)enables a user to generate commands in the form of waypoint file 2206(FIG. 21) for execution by robot 2102 which results in the formation ofa virtual rail or track that is followed or traced by robot 2102. Thevirtual track or rail may be created from an abstraction or may begenerated with reference to an available map or other boundarydesignations of the operating environment. Furthermore, accuratepositioning of the robot 2102 (FIG. 19) may be maintained by applicationof Markov localization techniques that may combat problems such asodometry drift. Generation of waypoint file 2206 (FIG. 21) allows arobot 2102 (FIG. 19), given accurate position data, to traverse a traceof arcs and lines at various speeds. The various embodiments of thepresent invention may utilize various mapping or localization techniquesincluding positioning systems such as indoor GPS, outdoor GPS and DGPS,a theodolite system as well as others which may be devised in thefuture.

As stated in FIG. 22, desired path 2422 includes a plurality of linesegments 2424-2432. Through the use of the user interface 2420 startpoint 2440 and end point 2442 may be selected with each individual linesegment 2424-2432 being individually selected thereby allowing theassociation of a velocity therewith. By way of example, line segment2432 is illustrated as being selected with a representative speed of 0.5meters per second being associated therewith. The path plan process 2204(FIG. 21) through user interface 2420 uses the properties of eachsegment within drawing file 2202 (FIG. 21) to spatially locate eachsegment (e.g., line or arc) and then creates a default path based on theinitial order of segments found in the drawing file 2202.

Path plan process 2204 (FIG. 21), through user interface 2420, can beused to manipulate various properties of the initial desired path 2422.For example, when segment 2432 is selected, the segment is highlightedin the user interface 2420. Once a segment is highlighted, itsproperties are displayed and can be edited, if desired. The order ofsegments can be changed, for example, either by using the “Move Up” and“Move Down” buttons or by selecting and dragging a segment to its newposition. Each segment can be included or excluded, for example, fromthe path by appropriately marking the “Include this entity in the path”checkbox. This allows additional features that are not part of the pathto be included in the drawing file without the requirement that they bea part of the virtual track or rail. Additional input boxes may beprovided to set the initial speed, the final speed or constantacceleration, and provide for comments for each segment.

Once motion characteristics, such as velocity, have been associated witheach of the line segments 2424-2432, other processing may be performedsuch as an estimation of run time as well as verification of velocitytransitions 2210 (FIG. 21). Once velocities have been associatedtherewith and verification of compatibility with the capabilities of thetarget robot have been performed, a waypoint file 2206 (FIG. 21) may begenerated by activating generate waypoint process 2212 (FIG. 21) withinthe user interface 2420.

FIG. 23 is a diagram illustrating transformation according to path planprocess 2204 (FIG. 21) from a drawing file to a waypoint file, inaccordance with an embodiment of the present invention. A drawing file2202 as received and generated in a user interface 2120 is transformedas stated above, with respect to FIG. 21, from a drawing file 2202 to awaypoint file 2206 according to path plan process 2204 (FIG. 21). Asstated, drawing file 2202 is a limited expression of graphical segmentsand must be augmented through path plan process 2204 to include, amongother things, motion characteristics such as velocities as well asexecution ordering of the segments. Additionally, for the generation ofwaypoint file 2206, the information in drawing file 2202 also undergoesverifications to determine if input velocities 2200 (FIG. 21) are withinthe capabilities of the robot.

By way of example and not limitation, waypoint file 2206 assumes one ormore formats, an example of which is illustrated with respect to FIG.23. Waypoint file 2206 may include an estimated traversal time 2402identifying a summation of the traversal times of each segment of thevirtual track. By way of example, waypoint file 2206 includes a listingof ordered vertices identifying the waypoints 2404-2414 for traversal bythe robot 102 (FIG. 19). Each waypoint 2404-2414 includes a waypointnumber indexed according to order as previously described, X and Ycoordinate values, a velocity value, and an arc continuation flag forassociating a set of waypoints for short line segments that comprise anarc traversal.

FIG. 24 is a functional block diagram of a control process of a robot,in accordance with an embodiment of the present invention. Robot controlprocess 2300 executes a waypoint file to trace-out or follow a virtualtrack or rail first defined and processed within the control generationsystem 2104 (FIG. 19). Robot control process 2300 includes alocalization process 2302 wherein the robot processes environmentalparameters including physical boundaries to determine a present frame ofreference for use in alignment or referencing the virtual track or rail.Localization is a continuous process that the robot uses to determineits present location with reference to its internal map. For example,when the robot first starts executing the waypoint file 2304, therobot's start point is the origin (0,0) with positive X in the forwarddirection for referencing the internal map. As the robot moves around,the robot locates items in the operating environment and then placesthose items in the robot's internal map. As the robot continues to move,the robot may encounter familiar features with an expectation that therecognized features are located in the same relative position. However,if the features have moved relative to where the robot believes thefeatures should be, then the robot assumes that the robot may not be inthe right place on the internal map. The continual correction of therobot's position in the internal map may be described as “localization.”

The localization process of the robot allows the robot to accurately andrepeatedly trace the waypoints forming the virtual rail or track. Thenavigation process 2306 responds to the localization process 2302 andsensor data from sensor process 2310 to generate controls to the robotmotion process 2308. Additionally, the robot uses sensor data fromsensor process 2310 to determine surrounding features. The robot controlprocess 2300 does not need to necessarily identify the composition oridentity of the features, but only the fact that they are part of theenvironment which forms boundaries for the robot. Robot 2102 may utilizeone or more sensors for providing feedback to the localization process.Sensors may include wheel measuring devices, laser sensors, ultrasonicsensors, and the like.

Waypoint navigation process 2306 generates commands or control signalsto a robot motion process 2308. Robot motion process 2308 generatescontrols to actuators for generating motion, rotation, etc., as well asvelocities associated therewith. Waypoint navigation process 2306further receives from sensor process 2310 sensor information in the formof feedback for determining when traversal of one or more segments ofthe virtual rail has been accomplished. Sensor process 2310 may alsoprovide information to waypoint navigation process 2306 in the form ofchanges to environmental parameters which enables waypoint navigationprocess 2306 to protect or guard against unforeseen changes to theenvironment. Additional details with respect to waypoint navigation aredescribed below with respect to FIGS. 26-28.

FIG. 25 is a flow chart 2500 of a method for implementing a virtualtrack for a robot, in accordance with an embodiment of the presentinvention. An execution of a robot traversing a virtual rail isinitiated by a user developing a desired path for the robot to followusing a drawing package to generate 2510 a drawing file.

As stated, a drawing file or other illustration of a desired path isgenerated 2510 and includes at least one segment representative of thevirtual track to be configured for the virtual track which the robotwill traverse. Generation of a desired path results in the creation of aspecific file format representing the illustrated segments of thedesired path. The file format, in one embodiment of the presentinvention, is converted 2520 into a standardized file format, an exampleof which is the .dxf format. Generation 2510 and converting 2520 stepsmay be accomplished through the use of one or more applications whichare made usable through a user interface, such as user interface 120(FIG. 20).

Through path plan process 2204 (FIG. 21) and as further illustrated withrespect to a user interface 2420 (FIG. 22), the drawing file 2202 (FIG.23) is imported 2530 and start points 2440 (FIG. 22), end points 2442(FIG. 22) and segment ordering may be assigned 2540 to the varioussegments of the desired path 2422 (FIG. 22). Through verificationprocess 2210 (FIG. 21), continuity may be checked or verified 2550 andinput velocities 2200 (FIG. 21) may be assigned 2560 to the varioussegments 2424-2432 (FIG. 22) of the desired path 2422 (FIG. 22). Furtherchecking and reporting 2570 of inconsistencies or incompatibilities mayalso be performed.

Once the desired path has been illustrated and start and end points aswell as velocities have been associated therewith as well as asuccessful completion of verification processes, a waypoint list 2206(FIG. 23) is generated 2580 and stored in a waypoint file. Uponcompletion of the generation of waypoint file 2206 (FIG. 21) by controlgeneration system 104 (FIG. 19), the waypoint file 2206 is sent 2590 viaa communication interface 106 (FIG. 19) to a robot 102 (FIG. 19).Thereafter, robot 102 may execute 2600 a first waypoint from waypointfile 2206 and subsequently execute 2610 a second and subsequentwaypoints using waypoint navigation process 2306 (FIG. 24).

4.5. Waypoint Following Behavior

FIGS. 26, 27, and 28 are software flow diagrams illustratingrepresentative algorithms for performing waypoint following according toembodiments of the present invention. The waypoints may come from analgorithm such as the virtual robot rail system described above,interaction with an operator, interaction with other robots, internallygenerated, or combinations thereof. FIG. 26 illustrates components of ahandler algorithm for handling transitions between waypoints, FIG. 27illustrates handling of translational velocities during waypointfollowing, and FIG. 27 illustrates handling of rotational velocitiesduring waypoint following.

The waypoint handler, illustrated in FIG. 26, starts with decision block902 to test whether path planning is active and the time since theachieving the last waypoint is greater than a threshold. In therepresentative embodiment of FIG. 26, the threshold is set at threeseconds. If sufficient progress has not been made toward a waypointwithin the threshold, there may be a barrier blocking the robot'sprogress toward the waypoint. For example, perhaps a door was closedthat the waypoint planning had assumed was open, or perhaps a newobstacle was placed in the environment such that the robot cannot find away around the obstacle to achieve the next way point. In these types ofcircumstances, it may be appropriate to plan a new path with a newwaypoint list. Thus, if path planning is active and the threshold isexceeded, operation block 904 performs a routine to delete the currentwaypoint list and plan a new waypoint list, then control transfers todecision block 906.

If decision block 902 evaluates false, or operation block 904 completes,decision block 906 test to see if the current waypoint is defined aspart of an arc. If the current waypoint is part of an arc, operationblock 908 sets a variable named Waypoint_Radius as the current speedtimes one half of the robot's length. This Waypoint_Radius variable isused later as a test threshold when determining how close the robot isto the waypoint. If the current waypoint is not part of an arc,operation block 910 sets Waypoint_Radius to one half the robot's lengthplus one half the length of the arm extension. Thus, the waypoint radiusis defined as the physical extent of the robot from the Robot's center.

With the Waypoint_Radius variable set, decision block 912 tests to seeif the angle to the target waypoint is currently less than 90 degrees tothe left or right. If so, operation block 914 sets the range to thetarget as the closest range within plus or minus 15 degrees of thecurrent angle to the target. If the waypoint is not less than 90 degreesaway, the range to target is set as Min Front_Distance, which, asexplained earlier, is the range to the nearest object within plus orminus 90 degrees of the robot's forward direction. The current angle tothe target defines the angle towards the target relative to straightahead. However, Range_To_Target defines a range (i.e., distance) fromthe robot to an obstacle in the direction of the waypoint.

After setting the Range_To_Target variable, decision block 918 tests tosee if the distance to the current waypoint is less than the waypointradius defined previously. If so, the waypoint is considered to beachieved, so operation block 920 iterates to the next waypoint in thewaypoint list, and the process exits.

If decision block 918 evaluates false, a more specific test is performedto see if the waypoint has been achieved. In some instances, it may notbe possible to actually place the center of the robot over the waypoint.For example, the waypoint may have been placed to close to a wall, orperhaps even behind the wall. However, if the robot can get closeenough, it may be sufficient to say the waypoint has been achieved.Thus, if decision block 922 evaluates true, operation block 920 iteratesto the next waypoint in the waypoint list, and the process exits.However, if decision block 922 evaluates false the process exits andcontinues on with the current waypoint.

A representative evaluation of a test for close enough to a waypoint isillustrated in block 924. Of course, those of ordinary skill in the artwill recognize that other parameters, distances, and decisions may bemade within the scope of the present invention to define whether awaypoint has been achieved. In block 924, the first test checks to seeif the Range_to_Target variable is less than the arm extension plus thelarger of a forward threshold or a side threshold. If not, there maystill be room to move forward or rotate toward the waypoint, so theprocess may exit and continue on with the current waypoint. Otherwise,the second test checks to see if the distance to the waypoint is lessthan the sum of the ram extension and the robot length. If not, theremay still be room to move forward or rotate toward the waypoint, so theprocess may exit and continue on with the current waypoint. Otherwise,the third test checks to see if the distance to the closest object onthe front side of the robot (i.e., Min_Front_Distance) is less than thearm extension plus twice the forward threshold. If not, there may stillbe room to move forward or rotate toward the waypoint, so the processmay exit and continue on with the current waypoint. Otherwise, the finalcheck tests to see if the angle to the target is less than 45 degrees orthe range to the nearest obstacle is less than the turn threshold. Ifnot, there may still be room to move or rotate toward the waypoint, sothe process may exit and continue on with the current waypoint.Otherwise, operation block 920 iterates to the next waypoint in thewaypoint list, and the process exits.

FIG. 27 is a software flow diagram illustrating components of analgorithm for adjusting translational velocity 930 during the waypointfollow behavior. First, operation block 932 tests to see if the distanceto the next waypoint is less than one tenth of the robot's length. Ifso, operation block 934 performs an update of the robot's current poseto be certain that the pose is precise relative to the waypointlocation.

If operation block 932 evaluates false, or after the pose is updated,decision block 936 tests to see if the range to the closest object infront is less than twice a predefined threshold. If not, controltransfers to decision block 944. However, if the range to the closestobject in front is less than twice a predefined threshold, the robot maybe approaching close to an obstacle, so decision block 938 tests to seeif the robot is blocked in the direction of the target. If so, operationblock 940 performs a backup procedure and the process exits. If therobot is not blocked in the target direction, decision block 942 teststo see if the angle to the target is greater than 60 degrees. If so, therobot may not be able to achieve the target without backing up, sooperation block 940 performs a backup procedure and the process exits.If the angle to the target is not greater than 60 degrees, a backupprocedure may not be needed and control transfers to decision block 944.

Decision block 944 tests to see if the angle to the target is greaterthan 45 degrees. If so, operation block 946 sets the translational speedto zero enabling the robot to stop making forward progress while itrotates to face more directly toward the target. After setting the speedto zero, the process exits.

If the angle to the target is not greater than 45 degrees, newtranslational velocity determination continues by decision block 948testing to see if a detection behavior is in progress. As stated earlierwhen describing the obstacle avoidance behavior, a detection behaviormay be a behavior where the robot is using a sensor in an attempt tofind something. For example, the countermine conduct is a detectionbehavior that is searching for landmines. In these types of detectionbehaviors, it may be desirable to approach much closer to objects, or toapproach objects with a much slower speed to allow time for thedetection function to operate. Thus, if a detection behavior is active,operation block 950 sets a desired speed variable based on detectionparameters that may be important. By way of example, and not limitation,in the case of the countermine conduct this desired speed may be set as:Desired_Speed=Max_passover_rate−(Scan_amplitude/Scan Speed). In thiscountermine conduct example, the Max_passover_rate may indicate amaximum desired speed for passing over the landmine. This speed may bereduced by other factors. For example, the (Scan_amplitude/Scan Speed)term reduces the desired speed based on a factor of how fast the minesensor sweeps an area. Thus, the Scan_amplitude term defines a term ofthe extent of the scan sweep and the Scan_Speed defines the rate atwhich the scan happens. For example, with a large Scan_amplitude and asmall Scan_Speed, the Desired_Speed will be reduced significantlyrelative to the Max_passover_rate to generate a slow speed forperforming the scan. While countermine conduct is used as an example ofa detection behavior, those of ordinary skill in the art will recognizethat embodiments of the present invention may include a wide variety ofdetection behaviors, such as, for example, radiation detection, chemicaldetection, and the like.

If a detection behavior is not in progress, decision block 952 tests tosee if a velocity limit is set. In some embodiments of the invention, itmay be possible for the operator to set a velocity limit that the robotshould not exceed, even if the robot believes it may be able to safelygo faster. For example, if the operator is performing a detailed visualsearch, the robot may be performing autonomous navigation, while theoperator is controlling a camera. The operator may wish to keep therobot going slow to have time to perform the visual search.

If a velocity limit is set, operation block 954 sets the desired speedvariable relative to the velocity limit. The equation illustrated inoperation block 954 is a representative equation that may be used. The0.1 term is a term used to ensure that the robot continues to make veryslow progress, which may be useful to many of the robot attributes,behaviors, and conduct. In this equation, the Speed_Factor term is anumber from one to ten, which may be set by other software modules, forexample the guarded motion behavior, to indicate a relative speed atwhich the robot should proceed. Thus, the desired speed is set as afractional amount of the Max_Limit_Speed.

If a velocity limit is not set, operation block 956 sets the desiredspeed variable relative to the maximum speed set for the robot (i.e.,Max_Speed) with an equation similar to that for operation block 614except Max_Speed is used rather than Max_Limit_Speed.

After the Desired_Speed variable is set by block 950, 954, or 956,decision block 958 determines if the distance to the current waypoint isless than the current velocity plus a small safety factor. If not,operation block 968 sets the new translational speed for the robot tothe Desired_Speed variable and the process exits. However, if thecurrent waypoint is getting close, as determined by decision block 958evaluating true, decision block 960 determines if the current waypointis part of an arc. If so, operation block 962 sets the translationalspeed such that the robot can smoothly traverse the arc. Thus, operationblock 962 is a representative equation that sets the new translationalspeed as a function of the larger of either the angle to the target, orthe turn angle to the next waypoint. In other words, the translationvelocity will be reduced by setting the new speed to the current speedmultiplied by a fractional change factor. This fractional change factormay be defined as the cosine of the larger of either the angle to thetarget, or the turn angle to the next waypoint.

If the current waypoint is not part of an arc, it may still be desirableto slow the robot's translational speed down in preparation for turningtoward the next waypoint. Thus, operation block 964 is a representativeequation for setting the new translational speed for the robot bymultiplying the current speed by a different fractional change factor.This fractional change factor may be set as about (0.7+(0.3*COS(Next_Turn_Angle). In other words, the new speed will be set somewherebetween 70% and 100% of the current speed based on the angle towards thenext waypoint. If the angle is small, for example zero degrees, theremay be no need to slow down and the new speed can be set at 100% of thecurrent speed. Conversely, if the angle is large, for example 90degrees, it may be desirable to slow down significantly in preparationfor a turn to the new waypoint. Thus, the new translational velocity isset at 70% of the current speed. Of course, the next time through theglobal timing loop presents another chance to adjust the translationalspeed if the angle to the next waypoint is still large.

This sets the translational speed based on the severity of the turn thatwill be negotiated to achieve the next waypoint.

After setting the current speed, from operation block 968, 964, or 962,the translational velocity process 930 ends.

FIG. 28 is a software flow diagram illustrating components of analgorithm for performing rotational velocity adjustments 970 of thewaypoint follow behavior. First, decision block 972, checks to see ifwaypoint following is enabled. If not, the process exits, becauserotational velocity changes will be handled by another behavior, suchas, for example, the obstacle avoidance behavior.

If waypoint following is enabled, decision block 974 tests to see if therobot is blocked in front. If not, rotational velocity determination cancontinue at decision block 986. However if the robot is blocked infront, decision block 976 determines whether the current waypoint is tothe left of the robot. If so, decision block 978 tests the area to theleft of the robot where the robot may want to turn towards and finds therange to the nearest object in that area. If the range is larger than aturning threshold, as tested by decision block 982, there is room toturn, so operation block 980 sets the rotational velocity to the left at30% of a predefined maximum rotational velocity. After setting therotational velocity, the process exits.

If the waypoint is not on the left, decision block 82 tests the area tothe right of the robot where the robot may want to turn towards andfinds the range to the nearest object in that area. If the range islarger than a turning threshold, as tested by decision block 982, thereis room to turn, so operation block 984 sets the rotational velocity tothe right at 30% of a predefined maximum rotational velocity. Aftersetting the rotational velocity, the process exits.

If the robot is blocked in front and there is not room to turn (i.e.,either decision block 978 or 982 evaluates false), then the processexits to a get unstuck behavior in an effort to find away to get aroundthe obstacle in front so that the robot can continue to pursue thecurrent waypoint.

If the robot is not blocked in front, decision block 986 tests to see ifthe angle to the waypoint target is less than ten degrees. If so, therobot is close to pointed in the correct direction and only minorcorrections may be useful. Thus, in a representative method fordetermining an appropriate change to the rotational velocity, operationblock 988 sets a Waypoint_Turn_Gain as the angle to the target dividedby 100. Conversely, if the waypoint target is equal to or greater thanten degrees, a larger correction to the rotational velocity may beappropriate to get the robot pointed toward the current waypoint. Thus,in a representative method for determining an appropriate change to therotational velocity, operation block 990 sets a Waypoint_Turn_Gain asthe base 10 logarithm of the angle to the target minus one. As a result,the larger the angle to the waypoint target, the larger the value willbe for the Waypoint_Turn_Gain.

With the Waypoint_Turn_Gain set, decision block 992 test to see if thewaypoint is on the left. If so, operation block 994 sets the turnvelocity to the left by multiplying the current turn velocity by theWaypoint_Turn_Gain, and the process exits. If the waypoint is not on theleft, operation block 996 sets the turn velocity to the right bymultiplying the current turn velocity by the Waypoint_Turn_Gain, and theprocess exits.

As with other behaviors, the waypoint algorithms 900, 930, and 970, inFIGS. 26, 27, and 28, respectively operate on the global timing loop.Consequently, the decision of whether a waypoint has been achieved tomove on to the next waypoint, adjustments to the translational velocity,and adjustments to the rotational velocity, may be repeated on each timetick of the global timing loop.

4.6. Robotic Follow Conduct

One representative cognitive conduct module enabled by the RIK is arobotic follow capability wherein one or more robots are sent to a maplocation of the most recent change in the environment or directed tofollow a specific moving object. FIG. 29 is a software flow diagramillustrating components of an algorithm for performing the followconduct 1000.

This relatively autonomous conduct may be useful for a fast-moving robotwith perceptors that may be used by robot attributes and robot behaviorsto detect and track changes in the environment. It would be difficultfor conventional robots under direct operator control to avoidobstacles, track where the robot is going, and track the object ofpursuit at the same time. However, with the relatively autonomousconduct and collaborative tasking enabled by the RIK, a high-speed chasemay be possible.

The RIK may include a tracking behavior that allows the robot to trackand follow an object specified by the operator with the camera or othertracking sensors, such as thermal, infrared, sonar, and laser.Consequently, the tracking behavior is not limited to visual tracking,but can be used with any tracking system including a thermal imagingsystem for tracking human heat signatures.

In visual tracking, for example, the operator may specify an object tobe tracked within the operator's video display by selecting a pursuitbutton on the interface and then manipulating the camera view so thatthe object to be tracked is within a bounding box. The camera can thentrack the object based on a combination of, for example, edge detection,motion tracking, and color blob tracking. Furthermore, the camera cantrack the motion of the target independently from the motion of therobot, which allows the robot to follow the optimal path aroundobstacles even if this path at first may take the robot in a directiondifferent from the direction of the target.

Thus, the robotic follow conduct 1000 effectively blends robotbehaviors, such as, for example, tracking, obstacle avoidance, reactivepath planning, and pursuit behaviors. To begin the follow conduct 1000,operation block 1010 illustrates that the conduct queries or receivesinformation regarding the present bearing to a target. This presentbearing may be generated by a tracking behavior, such as, for example,the ROCA behavior discussed above or from an updated map location fromthe operator or other robot. In addition, the bearing may be convertedto a robot relative coordinate system, if needed. Both the trackingbehavior and follow conduct 1000 operate on the global timing loop. As aresult, the follow conduct 1000 will be re-entered each timing tick andbe able to receive an updated bearing to the target, from the trackingbehavior or other defined location, each timing tick.

Decision block 1015 tests to see if the robot has reached the target. Ifso, the follow conduct 1000 exits. If not, the follow conduct 1000transitions to decision block 1020. In this representative embodiment,reaching the target is defined as: 1) The Closest obstacle in a 30°region in which the tracked object lies is closer than the closestobstacle in a 30° region on the opposite side; 2) Both L-front andR-front are obstructed; 3) the angle to the object lies in the frontregion; and 4) the distance to the object in front is less than thedistance on the right and left.

Decision block 1020 tests to see if the front is blocked. If so, controltransfers to operation block 1030 to attempt to get around the obstacle.If not, control transfers to decision block 1070. The front blockeddecision may be based, for example, on a flag from the guarded motionbehavior discussed previously.

Decision block 1030 begins a process of attempting to get around aperceived obstacle. To begin this process, decision block 1030 checksthe current speed. If the speed is not greater than zero, controltransfers to decision block 1040. If the speed is greater than zero,operation block 1035 sets the speed to zero before continuing withdecision block 1040.

Decision block 1040 tests to see if the robot is blocked on the left orright. If not, control transfers to decision block 1050. If the robot isblocked on the left or right, the robot may not have an area sufficientto make a turn, so the robot is set to begin backing up with an angularvelocity of zero and a linear velocity that is 20% of the presentlyspecified maximum, then the follow conduct 1000 exits.

Decision block 1050 tests to see if the robot is blocked in thedirection of the target. If so, control transfers to decision block1060. If the robot is not blocked in the direction of the target, therobot is set to turn toward the target with a linear velocity of zeroand an angular velocity that is 60% of the presently specified maximum,then the follow conduct 1000 exits.

Decision block 1060 tests to see if the target is positionedsubstantially in front of the target. If so, the target is in front ofthe robot, but the robot is also blocked by an obstacle. Thus, operationblock 1062 attempts to move forward slowly but also turn around theobstacle by setting the linear velocity to 10% of the presentlyspecified maximum and the angular velocity to 60% of the presentlyspecified maximum and away from the obstacle. Then, the follow conduct1000 exits.

If decision block 1060 evaluates false, then the direction directly infront of the robot is blocked, and the direction toward the target isblocked. Thus, operation block 1064 attempts to find a clear path to thetarget by setting the linear velocity to −20% of the presently specifiedmaximum (i.e., backing up) and the angular velocity to 30% of thepresently specified maximum and in the direction of the target. Then,the follow conduct 1000 exits.

Returning to decision block 1020, if decision block 1020 evaluatesfalse, then decision block 1070 begins a process of attempting toprogress toward the target since the front is not blocked. Thus,decision block 1070 tests to see if the robot is blocked in thedirection of the target. If so, operation block 1075 attempts to moveforward while gradually turning away from the target in an effort to tryto find a clear path to the target by setting the linear velocity to 20%of the presently specified maximum and the angular velocity to 20% ofthe presently specified maximum. Then, the follow conduct 1000 exits.

If decision block 1070 evaluates false, then the target is not in frontof the robot and the robot is free to move forward. Thus, operationblock 1080 attempts to move forward and turn toward the target. In thisrepresentative embodiment, the robot is set with an angular velocitytoward the target that is determined by the current bearing toward thetarget divided by a predetermined turn factor. Consequently, the speedat which the robot attempts to turn directly toward the target may beadjusted by the turn factor. In addition, the robot is set to moveforward at a safe speed, which may be set as 10% of the maximum, toensure the robot keeps moving, plus a safe speed adjustment. The safespeed adjustment may be defined as Front-forward_threshold)/2. WhereinFront defines the distance to the nearest object in the vicinity ofdirectly in front as defined by the range attribute discussed earlier,and forward_threshold defines a distance to which the robot may berelatively certain that objects are outside of its time horizon. Thus,the robot makes fast, but safe, forward progress while turning towardthe target, and the speed may be adjusted on the next time tick based onnew event horizon information.

As with other robot behaviors and cognitive conduct, the follow conduct1000 operates on the global timing loop. Consequently, the ROCA behavior700 will be re-entered and the process repeated on the next time tick.

4.7. Countermine Conduct

One representative cognitive conduct module enabled by the RIK is acountermine process. FIGS. 30A and 30B are software flow diagramsillustrating components of a countermine conduct module. Landmines are aconstant danger to soldiers during conflict and to civilians long afterconflicts cease, causing thousands of deaths and tens of thousands ofinjuries every year. Human minesweeping to find and remove mines is adangerous and tedious job. Mine-detecting robots may be better equippedand expendable if things go wrong. The countermine conduct 1100 in FIGS.30A and 30B illustrates a relatively autonomous conduct that may beuseful for finding and marking landmines based on a predetermined path.The predetermined path may be defined as a series of waypoints, or maybe simply defined as a straight path between the robots present positionand an end point. For example, the series of way points may be definedon a map to follow a road or to create broad coverage of a large area.Those of ordinary skill in the art will recognize that FIGS. 30A and 30Billustrate a high level decision and action process. Details of many ofthe behaviors, such as some movements of manipulators and details ofwhat comprises the sensing of a mine may not be described in detail.Furthermore, FIGS. 30A and 30B and the description herein may expressdetails of geometry and function related to a specific robotimplementation for explanation purposes. Embodiments of the presentinvention are not intended to be limited to these specificimplementation details.

To begin the countermine conduct 1100, an initiate task 1110 isperformed. Generally, this initiate task may be performed at thebeginning of a countermine sweep and would thus be performed once,outside of the global timing loop.

The initiate task may include operation block 1112 to fully raise asensing device, which may be configured for sensing landmines and may bepositioned on a manipulator for placement near the ground and forgenerating a sweeping motion of the mine sensor in a region around therobot. Operation block 1114 calibrates the sensing device and, forexample, corrects for background noise, if needed. Operation block 1116then positions the sensing device for operation and defines sensingparameters. As an example, the representative embodiment of FIG. 30Aillustrates setting a sweep amplitude, and a sweep speed for the minesensor.

After the initiate task 1110, the countermine conduct 1100 begins a fastadvance 1120 process by setting a relatively fast speed toward the firstwaypoint in operation block 1122. The fast advance speed may depend onmany factors, such as, for example, the motion capabilities of therobot, the sweeping characteristics of the manipulator, and the sensingcharacteristics of the mine sensor. Generally, the robot's fast advancespeed may be set relative to the sweep coverage of the manipulator toensure sufficient coverage of the area being swept. For example, in thisspecific embodiment operation block 1120 sets the robot's speed to about0.35 m/sec−(SweepWidth/10). Thus, Block 1120 actually determines themaximum advance rate based on scan width and scan speed to ensure 100%coverage. After setting the maximum advance rate, operation block 1124,enables the guarded motion and obstacle avoidance. On result of the fastadvance process 1120 is that the maximum advance rate serves as an upperbounds of allowable velocities for the guarded motion and obstacleavoidance behaviors as explained above.

Once in the fast advance mode 1120, the countermine conduct 1100 beginsa process of sensing for mines 1130. Decision block 1132 tests to see ifa signal processing threshold has been exceeded. This signal processingthreshold may be set at a predetermined level indicating a potentialthat a mine has been sensed in the vicinity of the mine sensor.Obviously, this predetermined threshold may be a function of factorssuch as, for example, expected mine types, mine sensor characteristics,robot speed, and manipulator speed. If the signal processing thresholdis not exceeded control returns to operation block 1122 to continue thefast advance 1120 process.

If the signal processing threshold is exceeded, the process tests to seeif there is enough room at the present location to conduct a detailedsearch for the mine. Thus, decision block 1134 tests to see if the frontrange parameter is larger than a predetermined threshold. By way ofexample, and not limitation, the threshold may be set at about onemeter. If decision block 1134 evaluates false, indicating that there maynot be enough room for a detailed search, control transfers to operationblock 1122 to continue the fast advance process 1120. In this case, theprocess depends on the guarded motion and obstacle avoidance behaviorsto navigate a path around the potential mine.

If the front range parameter is larger than a predetermined threshold,there may be room for a detailed search and the process continues.Decision block 1136 tests to see if the back of the robot is blocked. Ifso, operation block 1138 sets the robot to back up a predetermineddistance (for example 0.2 meters) at a speed of, for example, 20% of apredetermined maximum. This movement enable the robot to perform a moreaccurate sweep by including in the scan the subsurface area thattriggered the processing threshold. If the area behind the robot is notclear, the process continues without backing up.

Operation block 1140 performs a coverage algorithm in an attempt tosubstantially pinpoint the centroid of the possible mine location. In arepresentative embodiment, this coverage algorithm may include advancinga predetermined distance, for example 0.5 meters, at a relatively slowspeed, and sweeping the manipulator bearing the mine sensor with a widersweep angle and a relatively slow speed. Thus, the coverage algorithmgenerates a detailed scan map of the subsurface encompassing the areathat would have triggered the processing threshold. The results of thisdetailed scan map may be used to define a centroid for a mine, if found.

After the detailed scan from the coverage algorithm of block 1140,decision block 1152, on FIG. 30B, begins a process to marking the minelocation 1150, which may have been found by the coverage algorithm.Decision block 1152 tests to see if the centroid of a mine has beenfound. If not, control transfers to the end of the mine marking process1150. A centroid of a mine may not be found because the original coarsetest at decision block 1132 indicated the possibility of a mine, but thecoverage algorithm at operation block 1152 could not find a mine. As aresult, there is nothing to mark.

If a centroid was found, decision block 1154 tests to see if physicalmarking, such as, for example, painting the location on the ground, isenabled. If not, operation block 1156 saves the current location of thesensed mine, then continues to the end of the mine marking process 1150.

If marking is engaged, operation block 1158 saves the current locationof the mine, for example, as a waypoint at the current location. Next,operation block 1160 corrects the robot position in preparation formarking the location. For example, and not limitation, the robot mayneed to backup such that the distance between the centroid of the mineand the robot's current position is substantially near the arm length ofthe manipulator bearing the marking device.

With the robot properly positioned, operation block 1162 moves themanipulator bearing the marking device in proper position for making amark. For example of a specific robot configuration, and not limitation,the manipulator may be positioned based on the equation:

arm position=robot pose−arctan((robotx−centroidx)/roboty−centroidy))

With the manipulator in position, operation block 1164 marks the minelocation, such as, for example, by making a spray paint mark.

After completion of the mine marking process 1150, decision block 1166tests to see if the robot has reached the furthest waypoint in thepredefined path. If so, the countermine conduct 1100 has completed itstask and exits. If the further waypoint has not been reached, controlreturns to the fast advance process 1120 on FIG. 30A.

5. Multi-Robot Control Interface

Conventional robots lack significant inherent intelligence allowing themto operate at even the most elementary levels of autonomy. Accordingly,conventional robot “intelligence” results from a collection ofprogrammed behaviors preventing the robot from performing damaging andhurtful actions, such as refraining from getting stuck in corners orencountering obstacles.

While robots have great potential for engaging situations withoutputting humans at risk, conventional robots still lack the ability tomake autonomous decisions and therefore continue to rely on continuousguidance by human operators who generally react to live video from therobot's on-board cameras. An operator's user interface with a robot hasgenerally been limited to a real-time video link that requires ahigh-bandwidth communication channel and extensive human interaction andinterpretation of the video information.

Most commercial robots operate on a master/slave principle where a humanoperator controls the movement of the robot from a remote location inresponse to information from robot-based sensors such as video and GPS.Such an interface often requires more than one operator per robot tonavigate around obstacles and achieve a goal and such an approachgenerally requires highly practiced and skilled operators to reliablydirect the robot. Additionally, the requisite concentration needed forcontrolling the robot may also detract an operator from achieving theoverall mission goals. Accordingly, even an elementary search and rescuetask using a robot has typically required more than one operator tomonitor and control the robot. As robots become more commonplace,requiring an abundance of human interaction becomes inefficient andcostly, as well as error prone. Therefore, there is a need to provide ausable and extendable user interface between a user or operator and aplurality of robots.

Embodiments of the present invention provide methods and apparatuses formonitoring and tasking multiple robots. In the following description,processes, circuits and functions may be shown in block diagram form inorder not to obscure the present invention in unnecessary detail.Additionally, block definitions and partitioning of logic betweenvarious blocks is exemplary of a specific implementation. It will bereadily apparent to one of ordinary skill in the art that the presentinvention may be practiced by numerous other partitioning solutions. Forthe most part, details concerning timing considerations and the likehave been omitted where such details are not necessary to obtain acomplete understanding of the present invention and are within theabilities of persons of ordinary skill in the relevant art.

The various embodiments of the present invention are drawn to aninterface that supports multiple levels of robot initiative and humanintervention which may also provide an increased deployment ratio ofrobots to operators. Additionally, exchange of information between arobot and an operator may be at least partially advantageously processedprior to presentation to the operator thereby allowing the operator tointeract at a higher task level. Further improvements are also providedthrough tasking multiple robots and decomposing high-level user taskinginto specific operational behaviors for one or more robots.

FIG. 31 is a block diagram of a multi-robot system including amulti-robot user interface, in accordance with an embodiment of thepresent invention. A multi-robot system 3100 includes a team 3102 ofrobots 3104 including a plurality of robots 3104-1, 3104-N. Multi-robotsystem 3100 further includes a user interface system 3106 configured tocommunicate with the team 3102 of robots 3104 over respectivecommunication interfaces 3108-1, 3108-N.

By way of example and not limitation, the user interface system 3106,including input devices such as a mouse 3110 or joystick, enableseffective monitoring and tasking of team 3102 of robots 3104.Interaction between robots 3104 and user interface system 3106 is inaccordance with a communication methodology that allows information fromthe robots 3104 to be efficiently decomposed into essential abstractionswhich are sent over communication interfaces 3108 on a “need-to-know”basis. The user interface system 3106 parses the received messages fromrobots 3104 and reconstitutes the information into a display that ismeaningful to the user.

In one embodiment of the present invention, user interface system 3106further includes a user interface 3200 as illustrated with respect toFIG. 32. User interface 3200 is configured as a “cognitive collaborativeworkspace” which is configured as a semantic map overlaid withiconographic representations which can be added and annotated by humanoperators as well as by robots 3104. The cognitive collaborative natureof user interface 3200 includes a three-dimensional (3-D) representationthat supports a shared understanding of the task and environment. Userinterface 3200 provides an efficient means for monitoring and taskingthe robots 3104 and provides a means for shared understanding betweenthe operator and the team 3102 of robots 3104. Furthermore, userinterface 3200 may reduce human navigational error, reduce humanworkload, increase performance and decrease communication bandwidth whencompared to a baseline teleoperation using a conventional robot userinterface.

In contrast to the static interfaces generally employed for control ofmobile robots, user interface system 3106 adapts automatically tosupport different modes of operator involvement. The environmentrepresentation displayed by the user interface 3200 is able to scale todifferent perspectives. Likewise, the user support and tasking toolsautomatically configure to meet the cognitive/information needs of theoperator as autonomy levels change.

A functional aspect of the user interface is the cognitive,collaborative workspace which is a real-time semantic map, constructedcollaboratively by human and machines, that serves as the basis for aspectrum of mutual human-robot interactions including tasking, situationawareness, human-assisted perception and collaborative environmental“understanding.” The workspace represents a fusion of a wide variety ofsensing from disparate modalities and from multiple robots.

Another functional aspect of the user interface is the ability todecompose high-level user tasking into specific robot behaviors. Userinterface system 3106 may include capabilities for several autonomousbehaviors including area search, path planning, route following andpatrol. For each of these waypoint-based behaviors, the user interfacesystem 3106 may include algorithms which decide how to break up thespecified path or region into a list of waypoints that can be sent toeach robot.

The collaborative workspace provided by the user interface 3200 providesa scalable representation that fuses information from many sensors,robots and operators into a single coherent picture. Collaborativeconstruction of an emerging map enhances each individual team robot'sunderstanding of the environment and provides a shared semantic lexiconfor communication.

User interface 3200 may support a variety of hardware configurations forboth information display and control inputs. The user interface may beadapted to the needs of a single operator/single robot team as well asto multi operator/multiple robot teams with applications varying fromrepetitive tasks in known environments to multi agent investigations ofunknown environments.

With reference to FIG. 31, control inputs to the robot can come from thekeyboard, mouse actions, touch screen, or joysticks. Controls based on,for example, the joystick are dynamically configurable. Any joystickdevice that the computer system will recognize can be configured to workin the user interface 3200.

By way of example and not limitation, an illustrative embodiment of userinterface 3200 is illustrated with respect to FIG. 32. Display ofinformation from the robot can be made on one or more monitors attachedto the user interface system. The user interface 3200 contains severalwindows for each robot on the team. These windows may include: a videowindow 3210, a sensor status window 3220, an autonomy control window3230, a robot window 3240 and a dashboard window 3250. Each of thesewindows is maintained, but not necessarily displayed, for each robotcurrently communicating with the system. As new robots announcethemselves to the user interface system 3106, then a set of windows forthat specific robot is added. In addition, a multi-robot common windowalso referred to herein as an emerging map window 3260 is displayedwhich contains the emerging position map and is common to all robots onthe team. The illustrative embodiment of the user interface 3200includes a single display containing, for example, five windows 3210,3220, 3230, 3240, 3250 and a common emerging map window 3260 asillustrated with respect to FIGS. 33-38.

FIG. 33 illustrates a video window 3210 of user interface 3200, inaccordance with an embodiment of the present invention. Video window3210 illustrates a video feed 3212 from the robot 3104 as well ascontrols for pan, tilt, and zoom. Frame size, frame rate, andcompression settings can be accessed from a subwindow therein andprovide a means for the user to dynamically configure the video tosupport changing operator needs.

FIG. 34 illustrates a sensor status window 3220 of user interface 3200,in accordance with an embodiment of the present invention. Sensor statuswindow 3220 includes status indicators and controls that allow theoperator to monitor and configure the robot's sensor suite as neededwhich permits the operator to know at all times which sensors areavailable, which sensors are suspect, and which are off-line. Inaddition, the controls allow the user to actually remove the data fromeach sensor from the processing/behavior refresh and monitoring loop.For example, the operator through the user interface may decide to turnoff the laser range finder if dust in the environment is interferingwith the range readings.

FIG. 35 illustrates an autonomy control window 3230 of user interface3200, in accordance with an embodiment of the present invention.Autonomy control window 3230 includes a plurality of selectable controlsfor specifying a degree of robot autonomy.

Additionally, in autonomy control window 3230, the user can selectbetween different levels of robot autonomy. Multiple levels of autonomyprovide the user with an ability to coordinate a variety of reactive anddeliberative robot behaviors. Examples of varying levels of autonomyinclude telemode, safe mode, shared mode collaborative tasking mode, andautonomous mode as described above with reference to FIGS. 10A and 10B.

User interface 3200 permits the operator or user to switch between allfour modes of autonomy as the task constraints, human needs and robotcapabilities change. For instance, the tele mode can he useful to pushopen a door or shift a chair out of the way, whereas the autonomous modeis especially useful if human workload intensifies or in an area wherecommunications to and from the robot are sporadic. As the robot assumesa more active role by moving up to higher levels of autonomy, theoperator can essentially “ride shotgun” and turn his or her attention tothe crucial tasks at hand—locating victims, hazards, dangerousmaterials; following suspects; measuring radiation and/or contaminantlevels—without worrying about moment-to-moment navigation decisions orcommunications gaps.

FIG. 36 illustrates a robot window 3240 of user interface 3200, inaccordance with an embodiment of the present invention. Robot window3240 pertains to movement within the local environment and providesindications of direction and speed of robot motion, obstructions,resistance to motion, and feedback from contact sensors. Robot window3240 indicates illustrative blockage indicators 3242 indicative ofimpeded motion in a given direction next to the iconographicrepresentation of the robot wheels indicating that movement right andleft is not possible because of an object too close to the left sidewheels. These blockage indicators 3232 allow the operator to understandwhy the robot 3104 has overridden a movement command. Since the visualindications can sometimes be overlooked, a force feedback joystick mayalso implemented to resist movement in the blocked direction. Thejoystick may vibrate if the user continues to command movement in adirection already indicated as blocked.

FIG. 37 illustrates a dashboard window 3250 of the multi-robot userinterface 3200, in accordance with an embodiment of the presentinvention. As illustrated, dashboard window 3250 contains informationabout the robot's operational status such as communication activity,power and feedback regarding the robot's pitch and roll. When drivingthe robot directly, operators may give directional commands using thejoystick. Dashboard window 3250 further includes a number of dials andindicators showing battery power level, speed, heading, pitch/roll,system health, and communications health.

FIG. 38 illustrates an emerging map window 3260 of the multi-robot userinterface 3200, in accordance with an embodiment of the presentinvention. As illustrated, the multi-robot common window or emerging mapwindow 3260 provides an emerging map 3262 of the environment and allowsthe operator to initiate a number of waypoint-based autonomous behaviorssuch as search region and follow path. In emerging map window 3260,controls are also present that enable an operator to zoom the map in andout. Unlike competitive products that require transmission of live videoimages from the robot to the operator for control, the user interfacesystem 3106 (FIG. 31) creates a 3D, computer-game-style representationof the real world constructed on-the-fly that promotes situationawareness and efficient tasking. Data for the dynamic representation isgathered using scanning lasers, sonar and infrared sensors that create aclear picture of the environment even when the location is dark orobscured by smoke or dust.

The emerging map 3262 is displayed in user interface 3200 andillustrates not only walls and obstacles but also other things that aresignificant to the operator. The operator can insert items—people,hazardous objects, etc. from a pull-down menu or still images capturedfrom the robot video—to establish what was seen and where. In this way,the representation is a collaborative workspace that supports virtualand real elements supplied by both the robot and the operator. Theemerging map 3262 also maintains the size relationships of the actualenvironment, helping the operator to understand the relative position ofthe robot in the real world. The operator may change the zoom, pitch andyaw of the emerging map 3262 to get other perspectives, including atop-down view of the entire environment—showing walls, obstacles,hallways and other topographical features.

The multi-robot user interface system 3106 (FIG. 31) is configured torecognize when communications are received from a new robot andinstantiates a new set of robot-centric control windows to allowindividual tasking of that robot. Likewise, the user interface 3200automatically displays and disseminates whatever information is relevantto the collaborative workspace (i.e. information to be shared such asvarious map entities and environmental features it may have discovered).

For the human team members, the current cognitive collaborativeworkspace, as illustrated with respect to user interface 3200, providespoint-and click user validation and iconographic insertion of mapentities. An operator can verify or remove entities, which have beenautonomously added and can add new entities. The user interface 3200also allows the workspace perspective to be focused on a single robot inwhich case it will track a selected robot and transform the data invarious interface windows to be relevant to that robot. By choosing to“free” the perspective, the user gains the ability to traverse theenvironment with a 3rd person perspective and monitor the task andenvironment as a whole. The multi-robot user interface 3200 may decidewhich windows to show/hide based on the level of autonomy and the choiceof focus.

FIG. 39 is a diagram of control processes within the robots and userinterface system 3106, in accordance with an embodiment of the presentinvention. FIG. 39 illustrates multiple processes 3300-1, 3300-N ofrobots 3104-1, 3104-N (FIG. 31) in the field being controlled by anoperator at user interface system 3106. The robot processes 3300-1,3300-N pass data about their state and the environment around them to anoperator at user interface system 3106. The operator sends commands andqueries to the robot processes 3300-1, 3300-N to direct the operation ofrobots 3104-1, 3104-N.

Robot processes 3300-1, 3300-N illustrate a distillation of the processof taking low level data and converting it to perceptual abstractionsthat are more easily grasped by an operator. These abstractions arepackaged in data packets according to a message protocol and thenfiltered for transmission to user interface system 3106. This protocolis composed of message packets that contain information on the type ofdata being passed, the robot that generated the message, the dataitself, and control characters for the packet. Each robot in the fieldhas a unique identifier that is contained in each data packet itgenerates. All robots use the same communication interface 3108-1,3108-N to the user interface system 3106. This abstraction andtransmission method allows long distance radio communications at lowbandwidth over a single channel for multiple robots, rather thanrequiring several channels of high bandwidth and close proximity aswould be required for passing raw data for analysis, as in most otherrobot control systems.

Regarding the user interface system 3106, data packets from the robotsin the field enter the user interface system 3106 and are deblocked andextracted to information usable by the user interface 3200 (FIG. 32).This data can go directly to the various windows 3210, 3220, 3230, 3240,3250, 3260, or the data can go through an interface intelligence package3330, which can provide further enhancement or abstractions of the datathat are meaningful to the operator. The various embodiments of thepresent invention contemplate at least two types of windows for eachindividual robot, display windows and control windows. Display windowsshow data in logical layouts and allow the operator to monitor therobot. The presentation of data in windows 3210, 3220, 3230, 3240, 3250,3260 may be partitioned into four types of windows, individual robotdisplay windows (e.g., sensor status window 3220, dashboard window3250), individual robot control windows (e.g., video window 3210, robotwindow 3240, autonomy control window 3230), and map and tasking controlwindows (e.g., emerging map window 3260).

The map and tasking control windows show a more global representationand contain all the robots together. They provide both display of dataand the control of robot function. The mapping and tasking controlwindows can also provide higher level control to perform specific tasks,such as area search, intruder detection, mine detection, and waypointand path generation. In order to provide the higher level tasking themap and tasking control windows rely on the interface intelligencepackage 3330.

Control messages from the user interface system 3106 follow a similarpath to the robots as the data abstractions follow from the robots tothe user interface system 3106. Command messages from the controlwindows (e.g., e.g., video window 3210, robot window 3240, autonomycontrol window 3230) are assembled in data packets which may include thesame structure (e.g., message type, robot to whom the packet isdirected, the data itself, and the packet control characters) as thepackets from the robots. The packets to the robots may be generated fromthe various control windows or the interface intelligence package 3330.The messages are packaged and sent through the robot interface server3320 and sent to the robots over the communication interface 3108-1,3108-N and then deblocked in the robot communication layer 3302 andacted on by the appropriate robot.

The user interface system 3106 does not make assumptions on the robots'states but utilizes acknowledgement of sent commands. For example, ifthe operator requests the robot to go to a specific point (e.g., settinga waypoint), the user interface system 3106 waits for the robot to sendback the waypoint before displaying the waypoint in the various windowson user interface 3200. By enforcing an acknowledgement, the actualrobot state is always presented, rather than an assumed state of therobot. However, the robot does not require the detection or interactionwith a user interface system 3106 in order to preserve safety andcomplete a requested task while also protecting itself from erroneousoperator maneuvers or directives. The robots may also constantlybroadcast their states over the communication interface thereby allowingthe user interface system to obtain current robot status. Since eachrobot is constantly broadcasting its data, and all data packets haveunique robot identifiers, the appearance of new robots in the messagestream automatically generate the appearance of the robot in the userinterface system.

Although this invention has been described with reference to particularembodiments, the invention is not limited to these describedembodiments. Rather, the invention is limited only by the appendedclaims, which include within their scope all equivalent devices ormethods that operate according to the principles of the invention asdescribed.

1. A method for providing a generic robot architecture for robot controlsoftware, comprising: providing a hardware abstraction level configuredfor developing a plurality of hardware abstractions for defining,monitoring, and controlling a plurality of hardware modules available ona robot platform; and providing a robot abstraction level configured fordefining a plurality of robot attributes comprising at least one of theplurality of hardware abstractions, wherein the plurality of robotattributes provide a software framework for building robot behaviorsfrom the plurality of robot attributes and wherein each robot attributeof the plurality is configured for substantially isolating the robotbehaviors from the plurality of hardware abstractions.
 2. The method ofclaim 1, wherein at least two hardware abstractions providesubstantially similar hardware information to at least one of theplurality of robot attributes, and wherein the at least one of theplurality of robot attributes combines the hardware information fromeach of the at least two hardware abstractions to form attributeinformation for the least one of the plurality of robot attributes. 3.The method of claim 2, wherein the at least one of the plurality ofrobot attributes disregards the hardware information from one of the atleast two hardware abstractions in forming the attribute information. 4.The method of claim 3, wherein the at least one of the plurality ofrobot attributes disregards the hardware information because thehardware module corresponding to the hardware information is absent ornon-functional.
 5. The method of claim 1, wherein at least two hardwareabstractions provide substantially different hardware information to atleast one of the plurality of robot attributes, and wherein the at leastone of the plurality of robot attributes combines the different hardwareinformation from each of the at least two hardware abstractions to formthe attribute information for the least one of the plurality of robotattributes when the attribute information involves a combination of thedifferent hardware information.
 6. The method of claim 1, wherein theplurality of hardware abstractions are selected from the groupconsisting of manipulation abstractions of manipulation type devices,communication abstractions of communication media and communicationprotocols, locomotion abstractions of locomotion hardware, andperception abstractions of perception type devices.
 7. The method ofclaim 1, wherein the plurality of robot attributes are selected from thegroup consisting of robot health, camera view, resistance to motion,robot position, robot motion, robot attitude, robot bounding shape, andrange.
 8. The method of claim 1, wherein providing the robot abstractionlevel further comprises providing a plurality of environmentabstractions wherein the plurality of environment abstractions provideenvironment information about an environment around the robot to anoperator, to the robot behaviors, to another robot, or to combinationsthereof.
 9. The method of claim 8, wherein the plurality of environmentabstractions are selected from the group consisting of an occupancygrid, a robot map position, an obstruction abstraction, and environmentfeature abstraction, a target abstraction, and an entity abstraction.10. The method of claim 1, wherein the robot behaviors are selected fromthe group consisting of reactive behaviors and deliberative behaviors.11. A computer readable medium having computer executable instructionsthereon, which when executed on a processor provide a generic robotarchitecture, comprising: a hardware abstraction level configured fordeveloping a plurality of hardware abstractions for defining,monitoring, and controlling a plurality of hardware modules available ona robot platform; and a robot abstraction level configured for defininga plurality of robot attributes comprising at least one of the pluralityof hardware abstractions, wherein the plurality of robot attributesprovide a software framework for building robot behaviors from theplurality of robot attributes and wherein each robot attribute of theplurality is configured for substantially isolating the robot behaviorsfrom the plurality of hardware abstractions.
 12. The computer readablemedium of claim 11, wherein at least two hardware abstractions providesubstantially similar hardware information to at least one of theplurality of robot attributes, and wherein the at least one of theplurality of robot attributes combines the hardware information fromeach of the at least two hardware abstractions to form attributeinformation for the least one of the plurality of robot attributes. 13.The computer readable medium of claim 12, wherein the at least one ofthe plurality of robot attributes disregards the hardware informationfrom one of the at least two hardware abstractions in forming theattribute information.
 14. The computer readable medium of claim 13,wherein the at least one of the plurality of robot attributes disregardsthe hardware information because the hardware module corresponding tothe hardware information is absent or non-functional.
 15. The computerreadable medium of claim 11, wherein at least two hardware abstractionsprovide substantially different hardware information to at least one ofthe plurality of robot attributes, and wherein the at least one of theplurality of robot attributes combines the different hardwareinformation from each of the at least two hardware abstractions to formthe attribute information for the least one of the plurality of robotattributes when the attribute information involves a combination of thedifferent hardware information.
 16. The computer readable medium ofclaim 11, wherein the plurality of hardware abstractions are selectedfrom the group consisting of manipulation abstractions of manipulationtype devices, communication abstractions of communication media andcommunication protocols, locomotion abstractions of locomotion hardware,and perception abstractions of perception type devices.
 17. The computerreadable medium of claim 11, wherein the plurality of robot attributesare selected from the group consisting of robot health, camera view,resistance to motion, robot position, robot motion, robot attitude,robot bounding shape, and range.
 18. The computer readable medium ofclaim 11, wherein the robot abstraction level further comprises aplurality of environment abstractions wherein the plurality ofenvironment abstractions provide environment information about anenvironment around the robot to an operator, to the robot behaviors, toanother robot, or to combinations thereof.
 19. The computer readablemedium of claim 18, wherein the plurality of environment abstractionsare selected from the group consisting of an occupancy grid, a robot mapposition, an obstruction abstraction, and environment featureabstraction, a target abstraction, and an entity abstraction.
 20. Thecomputer readable medium of claim 11, wherein the robot behaviors areselected from the group consisting of reactive behaviors anddeliberative behaviors.
 21. A robot platform, comprising: at least oneperceptor configured for perceiving environmental variables of interest;at least one locomotor configured for providing mobility to the robotplatform; a system controller configured for executing a generic robotarchitecture, the generic robot architecture comprising: a hardwareabstraction level configured for developing a plurality of hardwareabstractions for defining, monitoring, and controlling a plurality ofhardware modules available on the robot platform; and a robotabstraction level configured for defining a plurality of robotattributes comprising at least one of the plurality of hardwareabstractions, wherein the plurality of robot attributes provide asoftware framework for building robot behaviors from the plurality ofrobot attributes and wherein each robot attribute of the plurality isconfigured for substantially isolating the robot behaviors from theplurality of hardware abstractions.
 22. The robot platform of claim 21,wherein at least two hardware abstractions provide substantially similarhardware information to at least one of the plurality of robotattributes, and wherein the at least one of the plurality of robotattributes combines the hardware information from each of the at leasttwo hardware abstractions to form attribute information for the leastone of the plurality of robot attributes.
 23. The robot platform ofclaim 22, wherein the at least one of the plurality of robot attributesdisregards the hardware information from one of the at least twohardware abstractions in forming the attribute information.
 24. Therobot platform of claim 23, wherein the at least one of the plurality ofrobot attributes disregards the hardware information because thehardware module corresponding to the hardware information is absent ornon-functional.
 25. The robot platform of claim 21, wherein at least twohardware abstractions provide substantially different hardwareinformation to at least one of the plurality of robot attributes, andwherein the at least one of the plurality of robot attributes combinesthe different hardware information from each of the at least twohardware abstractions to form the attribute information for the leastone of the plurality of robot attributes when the attribute informationinvolves a combination of the different hardware information.
 26. Therobot platform of claim 21, wherein the plurality of hardwareabstractions are selected from the group consisting of manipulationabstractions of manipulation type devices, communication abstractions ofcommunication media and communication protocols, locomotion abstractionsof locomotion hardware, and perception abstractions of perception typedevices.
 27. The robot platform of claim 21, wherein the plurality ofrobot attributes are selected from the group consisting of robot health,camera view, resistance to motion, robot position, robot motion, robotattitude, robot bounding shape, and range.
 28. The robot platform ofclaim 21, wherein the robot abstraction level further comprises aplurality of environment abstractions wherein the plurality ofenvironment abstractions provide environment information about anenvironment around the robot to an operator, to the robot behaviors, toanother robot, or to combinations thereof.
 29. The robot platform ofclaim 28, wherein the plurality of environment abstractions are selectedfrom the group consisting of an occupancy grid, a robot map position, anobstruction abstraction, and environment feature abstraction, a targetabstraction, and an entity abstraction.
 30. The robot platform of claim21, wherein the robot behaviors are selected from the group consistingof reactive behaviors and deliberative behaviors.